From computers to cultivation: reconceptualizing evolutionary psychology.

PubMed

Barrett, Louise; Pollet, Thomas V; Stulp, Gert

2014-01-01

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on "cognitive integration" or the "extended mind hypothesis" in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human "mind-making" within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach.

From computers to cultivation: reconceptualizing evolutionary psychology

PubMed Central

Barrett, Louise; Pollet, Thomas V.; Stulp, Gert

2014-01-01

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on “cognitive integration” or the “extended mind hypothesis” in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human “mind-making” within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach. PMID:25161633

Bio-inspired algorithms applied to molecular docking simulations.

PubMed

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

[The history of development of evolutionary methods in St. Petersburg school of computer simulation in biology].

PubMed

Menshutkin, V V; Kazanskiĭ, A B; Levchenko, V F

2010-01-01

The history of rise and development of evolutionary methods in Saint Petersburg school of biological modelling is traced and analyzed. Some pioneering works in simulation of ecological and evolutionary processes, performed in St.-Petersburg school became an exemplary ones for many followers in Russia and abroad. The individual-based approach became the crucial point in the history of the school as an adequate instrument for construction of models of biological evolution. This approach is natural for simulation of the evolution of life-history parameters and adaptive processes in populations and communities. In some cases simulated evolutionary process was used for solving a reverse problem, i. e., for estimation of uncertain life-history parameters of population. Evolutionary computations is one more aspect of this approach application in great many fields. The problems and vistas of ecological and evolutionary modelling in general are discussed.

Knowledge Guided Evolutionary Algorithms in Financial Investing

ERIC Educational Resources Information Center

Wimmer, Hayden

2013-01-01

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…

Automated design of spacecraft systems power subsystems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Kordon, Mark; Mandutianu, Dan; Salcedo, Jose; Wood, Eric; Hashemi, Mona

2006-01-01

This paper discusses the application of evolutionary computing to a dynamic space vehicle power subsystem resource and performance simulation in a parallel processing environment. Our objective is to demonstrate the feasibility, application and advantage of using evolutionary computation techniques for the early design search and optimization of space systems.

Using modified fruit fly optimisation algorithm to perform the function test and case studies

NASA Astrophysics Data System (ADS)

Pan, Wen-Tsao

2013-06-01

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

On 10 to 30 m-scale fracture networks in Gale Crater: Contraction of fine-grained sediments due to drying or of frozen sediments due to cooling?

NASA Astrophysics Data System (ADS)

Sletten, Ronald; Hallet, Bernard

2014-05-01

The area in Gale Crater north of the Curiosity landing site has been identified as an alluvial fan [1] and features diverse geological units [2], some with abundant contraction cracks that delineate polygons on the order of 10-30 meters across. These polygons are much larger than the < 1m flagstones seen in Yellowknife by Curiosity [3] and are more suggestive of polygonal patterned ground seen at higher latitudes on Mars [4] and Earth; however, current conditions indicate that ground ice is not stable in Gale Crater [4]. Nevertheless, past conditions, e.g. obliquity changes, may have allowed permafrost to develop and ground ice to form. The domains between the larger polygons are several meters wide, which is consistent with cyclic ratcheting of ice-cemented permafrost (thermal contraction with fractures opening, debris infilling the fractures, and the fractures not closing fully when the ground warms and expands). On the other hand, the large-scale crack networks often seem to be associated with certain lithologic units, including the thinly-bedded, lightly-colored mudstones exposed at Yellowknife. This suggests that the contraction cracks defining these 10 to 30-m polygons, as well as those defining the < 1m flagstones, formed in moist fine-grained sediments that contracted upon desiccation. If the fractures were due to contraction of ice-cemented permafrost, they would be insensitive to the type of sediments they formed in because the mechanical properties would be dominated by ice. The interpretation of the larger-scale crack network is limited to satellite images since Curiosity did not visit this area, and to evidence about surface materials elsewhere in the vicinity of the rover. This evidence points to the former presence of flowing water in Gale Crater and existence of shallow lakes of relatively low salinity and near-neutral pH at Yellowknife [5]. The large amount of contraction in Yellowknife deposits is consistent with a desiccation origin in these deposits as they are fine-grained and contain expandable clay minerals as found by Curiosity [6]. The crack networks may help interpret the past environment in Gale crater. Whether they are formed by ice-cemented permafrost contraction or desiccating lacustrine deposits requires the presence of water; however, the latter case argues for much more extensive presence of liquid water. Acknowledgement: This research is supported by Mars Science Laboratory contract awarded to Malin Space Science Systems References: [1] Williams, R. M. E (2013) Science, 340(6136), 1068-1072., [2] Sumner, D.Y. et al. (2013) LPI Contributions, 1719, 1699, [3] Stewart, W. et al. (2013), GSA Abstracts. v. 45, no. 7, p.4, [4] Mellon, M. T. et al. (1997) J. Geophys. Res., 102(E11), 25617-25628, [5] Grotzinger, J. P (2013) 10.1126/science.1242777, [6] Vaniman, D. T (2013) 10.1126/science.1243480

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks. PMID:24428926

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, M.; Klimeck, G.; Hanks, D.

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

Reconstructing evolutionary trees in parallel for massive sequences.

PubMed

Zou, Quan; Wan, Shixiang; Zeng, Xiangxiang; Ma, Zhanshan Sam

2017-12-14

Building the evolutionary trees for massive unaligned DNA sequences is challenging and crucial. However, reconstructing evolutionary tree for ultra-large sequences is hard. Massive multiple sequence alignment is also challenging and time/space consuming. Hadoop and Spark are developed recently, which bring spring light for the classical computational biology problems. In this paper, we tried to solve the multiple sequence alignment and evolutionary reconstruction in parallel. HPTree, which is developed in this paper, can deal with big DNA sequence files quickly. It works well on the >1GB files, and gets better performance than other evolutionary reconstruction tools. Users could use HPTree for reonstructing evolutioanry trees on the computer clusters or cloud platform (eg. Amazon Cloud). HPTree could help on population evolution research and metagenomics analysis. In this paper, we employ the Hadoop and Spark platform and design an evolutionary tree reconstruction software tool for unaligned massive DNA sequences. Clustering and multiple sequence alignment are done in parallel. Neighbour-joining model was employed for the evolutionary tree building. We opened our software together with source codes via http://lab.malab.cn/soft/HPtree/ .

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

PubMed

Kumar, Sudhir; Stecher, Glen; Li, Michael; Knyaz, Christina; Tamura, Koichiro

2018-06-01

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

Eco-Evo PVAs: Incorporating Eco-Evolutionary Processes into Population Viability Models

EPA Science Inventory

We synthesize how advances in computational methods and population genomics can be combined within an Ecological-Evolutionary (Eco-Evo) PVA model. Eco-Evo PVA models are powerful new tools for understanding the influence of evolutionary processes on plant and animal population pe...

Learning, epigenetics, and computation: An extension on Fitch's proposal. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W. Tecumseh Fitch

NASA Astrophysics Data System (ADS)

Okanoya, Kazuo

2014-09-01

The comparative computational approach of Fitch [1] attempts to renew the classical David Marr paradigm of computation, algorithm, and implementation, by introducing evolutionary view of the relationship between neural architecture and cognition. This comparative evolutionary view provides constraints useful in narrowing down the problem space for both cognition and neural mechanisms. I will provide two examples from our own studies that reinforce and extend Fitch's proposal.

Learning Evolution and the Nature of Science Using Evolutionary Computing and Artificial Life

ERIC Educational Resources Information Center

Pennock, Robert T.

2007-01-01

Because evolution in natural systems happens so slowly, it is difficult to design inquiry-based labs where students can experiment and observe evolution in the way they can when studying other phenomena. New research in evolutionary computation and artificial life provides a solution to this problem. This paper describes a new A-Life software…

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Using concepts from biology to improve problem-solving methods

NASA Astrophysics Data System (ADS)

Goodman, Erik D.; Rothwell, Edward J.; Averill, Ronald C.

2011-06-01

Observing nature has been a cornerstone of engineering design. Today, engineers look not only at finished products, but imitate the evolutionary process by which highly optimized artifacts have appeared in nature. Evolutionary computation began by capturing only the simplest ideas of evolution, but today, researchers study natural evolution and incorporate an increasing number of concepts in order to evolve solutions to complex engineering problems. At the new BEACON Center for the Study of Evolution in Action, studies in the lab and field and in silico are laying the groundwork for new tools for evolutionary engineering design. This paper, which accompanies a keynote address, describes various steps in development and application of evolutionary computation, particularly as regards sensor design, and sets the stage for future advances.

Assessing the feasibility of hydrate deposition on pipeline walls--adhesion force measurements of clathrate hydrate particles on carbon steel.

PubMed

Nicholas, Joseph W; Dieker, Laura E; Sloan, E Dendy; Koh, Carolyn A

2009-03-15

Adhesive forces between cyclopentane (CyC5) hydrates and carbon steel (CS) were measured. These forces were found to be substantially lower than CyC5 hydrate-CyC5 hydrate particle measurements and were also lower than ice-CS measurements. The measured adhesive forces were used in a force balance to predict particle removal from the pipeline wall, assuming no free water was present. The force balance predicted entrained hydrate particles of 3 microns and larger diameter would be removed at typical operating flow rates in offshore oil and gas pipelines. These predictions also suggest that hydrate deposition will not occur in stabilized (cold) flow practices.

Evolutionary Computation for the Identification of Emergent Behavior in Autonomous Systems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Guillaume, Alexandre

2009-01-01

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

Exploring Evolutionary Patterns in Genetic Sequence: A Computer Exercise

ERIC Educational Resources Information Center

Shumate, Alice M.; Windsor, Aaron J.

2010-01-01

The increase in publications presenting molecular evolutionary analyses and the availability of comparative sequence data through resources such as NCBI's GenBank underscore the necessity of providing undergraduates with hands-on sequence analysis skills in an evolutionary context. This need is particularly acute given that students have been…

Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.

PubMed

Spirov, Alexander; Holloway, David

2013-07-15

This paper surveys modeling approaches for studying the evolution of gene regulatory networks (GRNs). Modeling of the design or 'wiring' of GRNs has become increasingly common in developmental and medical biology, as a means of quantifying gene-gene interactions, the response to perturbations, and the overall dynamic motifs of networks. Drawing from developments in GRN 'design' modeling, a number of groups are now using simulations to study how GRNs evolve, both for comparative genomics and to uncover general principles of evolutionary processes. Such work can generally be termed evolution in silico. Complementary to these biologically-focused approaches, a now well-established field of computer science is Evolutionary Computations (ECs), in which highly efficient optimization techniques are inspired from evolutionary principles. In surveying biological simulation approaches, we discuss the considerations that must be taken with respect to: (a) the precision and completeness of the data (e.g. are the simulations for very close matches to anatomical data, or are they for more general exploration of evolutionary principles); (b) the level of detail to model (we proceed from 'coarse-grained' evolution of simple gene-gene interactions to 'fine-grained' evolution at the DNA sequence level); (c) to what degree is it important to include the genome's cellular context; and (d) the efficiency of computation. With respect to the latter, we argue that developments in computer science EC offer the means to perform more complete simulation searches, and will lead to more comprehensive biological predictions. Copyright © 2013 Elsevier Inc. All rights reserved.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes

PubMed Central

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel

2017-01-01

Abstract Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson’s hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. PMID:28204787

Open Reading Frame Phylogenetic Analysis on the Cloud

PubMed Central

2013-01-01

Phylogenetic analysis has become essential in researching the evolutionary relationships between viruses. These relationships are depicted on phylogenetic trees, in which viruses are grouped based on sequence similarity. Viral evolutionary relationships are identified from open reading frames rather than from complete sequences. Recently, cloud computing has become popular for developing internet-based bioinformatics tools. Biocloud is an efficient, scalable, and robust bioinformatics computing service. In this paper, we propose a cloud-based open reading frame phylogenetic analysis service. The proposed service integrates the Hadoop framework, virtualization technology, and phylogenetic analysis methods to provide a high-availability, large-scale bioservice. In a case study, we analyze the phylogenetic relationships among Norovirus. Evolutionary relationships are elucidated by aligning different open reading frame sequences. The proposed platform correctly identifies the evolutionary relationships between members of Norovirus. PMID:23671843

Evolutionary Technologies: Fundamentals and Applications to Information/Communication Systems and Manufacturing/Logistics Systems

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Kawakami, Hiroshi; Tsujimura, Yasuhiro; Handa, Hisashi; Lin, Lin; Okamoto, Azuma

As efficient utilization of computational resources is increasing, evolutionary technology based on the Genetic Algorithm (GA), Genetic Programming (GP), Evolution Strategy (ES) and other Evolutionary Computations (ECs) is making rapid progress, and its social recognition and the need as applied technology are increasing. This is explained by the facts that EC offers higher robustness for knowledge information processing systems, intelligent production and logistics systems, most advanced production scheduling and other various real-world problems compared to the approaches based on conventional theories, and EC ensures flexible applicability and usefulness for any unknown system environment even in a case where accurate mathematical modeling fails in the formulation. In this paper, we provide a comprehensive survey of the current state-of-the-art in the fundamentals and applications of evolutionary technologies.

Computationally mapping sequence space to understand evolutionary protein engineering.

PubMed

Armstrong, Kathryn A; Tidor, Bruce

2008-01-01

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

Stochastic Evolutionary Algorithms for Planning Robot Paths

NASA Technical Reports Server (NTRS)

Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

2006-01-01

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

Biomimetic design processes in architecture: morphogenetic and evolutionary computational design.

PubMed

Menges, Achim

2012-03-01

Design computation has profound impact on architectural design methods. This paper explains how computational design enables the development of biomimetic design processes specific to architecture, and how they need to be significantly different from established biomimetic processes in engineering disciplines. The paper first explains the fundamental difference between computer-aided and computational design in architecture, as the understanding of this distinction is of critical importance for the research presented. Thereafter, the conceptual relation and possible transfer of principles from natural morphogenesis to design computation are introduced and the related developments of generative, feature-based, constraint-based, process-based and feedback-based computational design methods are presented. This morphogenetic design research is then related to exploratory evolutionary computation, followed by the presentation of two case studies focusing on the exemplary development of spatial envelope morphologies and urban block morphologies.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

PubMed

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel; Wegmann, Daniel

2017-11-01

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Big cat phylogenies, consensus trees, and computational thinking.

PubMed

Sul, Seung-Jin; Williams, Tiffani L

2011-07-01

Phylogenetics seeks to deduce the pattern of relatedness between organisms by using a phylogeny or evolutionary tree. For a given set of organisms or taxa, there may be many evolutionary trees depicting how these organisms evolved from a common ancestor. As a result, consensus trees are a popular approach for summarizing the shared evolutionary relationships in a group of trees. We examine these consensus techniques by studying how the pantherine lineage of cats (clouded leopard, jaguar, leopard, lion, snow leopard, and tiger) evolved, which is hotly debated. While there are many phylogenetic resources that describe consensus trees, there is very little information, written for biologists, regarding the underlying computational techniques for building them. The pantherine cats provide us with a small, relevant example to explore the computational techniques (such as sorting numbers, hashing functions, and traversing trees) for constructing consensus trees. Our hope is that life scientists enjoy peeking under the computational hood of consensus tree construction and share their positive experiences with others in their community.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

PubMed Central

Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

2011-01-01

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

Regulatory RNA design through evolutionary computation and strand displacement.

PubMed

Rostain, William; Landrain, Thomas E; Rodrigo, Guillermo; Jaramillo, Alfonso

2015-01-01

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing

PubMed Central

Nguyen, Nga Thi Thuy; Vincens, Pierre

2018-01-01

Abstract Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). PMID:29087490

Non-Evolutionary Algorithms for Scheduling Dependent Tasks in Distributed Heterogeneous Computing Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wayne F. Boyer; Gurdeep S. Hura

2005-09-01

The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized taskmore » orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,« less

Avoiding Local Optima with Interactive Evolutionary Robotics

DTIC Science & Technology

2012-07-09

the top of a flight of stairs selects for climbing ; suspending the robot and the target object above the ground and creating rungs between the two will...REPORT Avoiding Local Optimawith Interactive Evolutionary Robotics 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: The main bottleneck in evolutionary... robotics has traditionally been the time required to evolve robot controllers. However with the continued acceleration in computational resources, the

Optimality and stability of symmetric evolutionary games with applications in genetic selection.

PubMed

Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun

2015-06-01

Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

Evolutionary inference via the Poisson Indel Process.

PubMed

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Evolutionary inference via the Poisson Indel Process

PubMed Central

Bouchard-Côté, Alexandre; Jordan, Michael I.

2013-01-01

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114–124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments. PMID:23275296

VizieR Online Data Catalog: Comparison of evolutionary tracks (Martins+, 2013)

NASA Astrophysics Data System (ADS)

Martins, F.; Palacios, A.

2013-11-01

Tables of evolutionary models for massive stars. The files m*_stol.dat correspond to models computed with the code STAREVOL. The files m*_mesa.dat correspond to models computed with the code MESA. For each code, models with initial masses equal to 7, 9, 15, 20, 25, 40 and 60M⊙ are provided. No rotation is included. The overshooting parameter f is equal to 0.01. The metallicity is solar. (14 data files).

NexGen PVAs: Incorporating Eco-Evolutionary Processes into Population Viability Models

EPA Science Inventory

We examine how the integration of evolutionary and ecological processes in population dynamics – an emerging framework in ecology – could be incorporated into population viability analysis (PVA). Driven by parallel, complementary advances in population genomics and computational ...

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics

PubMed Central

Serohijos, Adrian W.R.; Shakhnovich, Eugene I.

2014-01-01

The variation among sequences and structures in nature is both determined by physical laws and by evolutionary history. However, these two factors are traditionally investigated by disciplines with different emphasis and philosophy—molecular biophysics on one hand and evolutionary population genetics in another. Here, we review recent theoretical and computational approaches that address the critical need to integrate these two disciplines. We first articulate the elements of these integrated approaches. Then, we survey their contribution to our mechanistic understanding of molecular evolution, the polymorphisms in coding region, the distribution of fitness effects (DFE) of mutations, the observed folding stability of proteins in nature, and the distribution of protein folds in genomes. PMID:24952216

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics.

PubMed

Serohijos, Adrian W R; Shakhnovich, Eugene I

2014-06-01

The variation among sequences and structures in nature is both determined by physical laws and by evolutionary history. However, these two factors are traditionally investigated by disciplines with different emphasis and philosophy-molecular biophysics on one hand and evolutionary population genetics in another. Here, we review recent theoretical and computational approaches that address the crucial need to integrate these two disciplines. We first articulate the elements of these approaches. Then, we survey their contribution to our mechanistic understanding of molecular evolution, the polymorphisms in coding region, the distribution of fitness effects (DFE) of mutations, the observed folding stability of proteins in nature, and the distribution of protein folds in genomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evolutionary trends in directional hearing

PubMed Central

Carr, Catherine E.; Christensen-Dalsgaard, Jakob

2016-01-01

Tympanic hearing is a true evolutionary novelty that arose in parallel within early tetrapods. We propose that in these tetrapods, selection for sound localization in air acted upon pre-existing directionally sensitive brainstem circuits, similar to those in fishes. Auditory circuits in birds and lizards resemble this ancestral, directionally sensitive framework. Despite this anatomically similarity, coding of sound source location differs between birds and lizards. In birds, brainstem circuits compute sound location from interaural cues. Lizards, however, have coupled ears, and do not need to compute source location in the brain. Thus their neural processing of sound direction differs, although all show mechanisms for enhancing sound source directionality. Comparisons with mammals reveal similarly complex interactions between coding strategies and evolutionary history. PMID:27448850

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing.

PubMed

Nguyen, Nga Thi Thuy; Vincens, Pierre; Roest Crollius, Hugues; Louis, Alexandra

2018-01-04

Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Phylogenetic tree and community structure from a Tangled Nature model.

PubMed

Canko, Osman; Taşkın, Ferhat; Argın, Kamil

2015-10-07

In evolutionary biology, the taxonomy and origination of species are widely studied subjects. An estimation of the evolutionary tree can be done via available DNA sequence data. The calculation of the tree is made by well-known and frequently used methods such as maximum likelihood and neighbor-joining. In order to examine the results of these methods, an evolutionary tree is pursued computationally by a mathematical model, called Tangled Nature. A relatively small genome space is investigated due to computational burden and it is found that the actual and predicted trees are in reasonably good agreement in terms of shape. Moreover, the speciation and the resulting community structure of the food-web are investigated by modularity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Scalable computing for evolutionary genomics.

PubMed

Prins, Pjotr; Belhachemi, Dominique; Möller, Steffen; Smant, Geert

2012-01-01

Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques. Unfortunately, parallel programming is difficult and requires special software design. The alternative, especially attractive for legacy software, is to introduce poor man's parallelization by running whole programs in parallel as separate processes, using job schedulers. Such pipelines are often deployed on bioinformatics computer clusters. Recent advances in PC virtualization have made it possible to run a full computer operating system, with all of its installed software, on top of another operating system, inside a "box," or virtual machine (VM). Such a VM can flexibly be deployed on multiple computers, in a local network, e.g., on existing desktop PCs, and even in the Cloud, to create a "virtual" computer cluster. Many bioinformatics applications in evolutionary biology can be run in parallel, running processes in one or more VMs. Here, we show how a ready-made bioinformatics VM image, named BioNode, effectively creates a computing cluster, and pipeline, in a few steps. This allows researchers to scale-up computations from their desktop, using available hardware, anytime it is required. BioNode is based on Debian Linux and can run on networked PCs and in the Cloud. Over 200 bioinformatics and statistical software packages, of interest to evolutionary biology, are included, such as PAML, Muscle, MAFFT, MrBayes, and BLAST. Most of these software packages are maintained through the Debian Med project. In addition, BioNode contains convenient configuration scripts for parallelizing bioinformatics software. Where Debian Med encourages packaging free and open source bioinformatics software through one central project, BioNode encourages creating free and open source VM images, for multiple targets, through one central project. BioNode can be deployed on Windows, OSX, Linux, and in the Cloud. Next to the downloadable BioNode images, we provide tutorials online, which empower bioinformaticians to install and run BioNode in different environments, as well as information for future initiatives, on creating and building such images.

MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis.

PubMed

Kumar, Sudhir; Stecher, Glen; Peterson, Daniel; Tamura, Koichiro

2012-10-15

There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. http://www.megasoftware.net/.

On joint subtree distributions under two evolutionary models.

PubMed

Wu, Taoyang; Choi, Kwok Pui

2016-04-01

In population and evolutionary biology, hypotheses about micro-evolutionary and macro-evolutionary processes are commonly tested by comparing the shape indices of empirical evolutionary trees with those predicted by neutral models. A key ingredient in this approach is the ability to compute and quantify distributions of various tree shape indices under random models of interest. As a step to meet this challenge, in this paper we investigate the joint distribution of cherries and pitchforks (that is, subtrees with two and three leaves) under two widely used null models: the Yule-Harding-Kingman (YHK) model and the proportional to distinguishable arrangements (PDA) model. Based on two novel recursive formulae, we propose a dynamic approach to numerically compute the exact joint distribution (and hence the marginal distributions) for trees of any size. We also obtained insights into the statistical properties of trees generated under these two models, including a constant correlation between the cherry and the pitchfork distributions under the YHK model, and the log-concavity and unimodality of the cherry distributions under both models. In addition, we show that there exists a unique change point for the cherry distributions between these two models. Copyright © 2015 Elsevier Inc. All rights reserved.

Evolutionary Computing Methods for Spectral Retrieval

NASA Technical Reports Server (NTRS)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary trees

PubMed Central

Lee, Bongshin; Nachmanson, Lev; Robertson, George; Carlson, Jonathan M.; Heckerman, David

2009-01-01

Summary: Evolutionary biologists are often interested in finding correlations among biological traits across a number of species, as such correlations may lead to testable hypotheses about the underlying function. Because some species are more closely related than others, computing and visualizing these correlations must be done in the context of the evolutionary tree that relates species. In this note, we introduce PhyloDet (short for PhyloDetective), an evolutionary tree visualization tool that enables biologists to visualize multiple traits mapped to the tree. Availability: http://research.microsoft.com/cue/phylodet/ Contact: bongshin@microsoft.com. PMID:19633096

Network-level architecture and the evolutionary potential of underground metabolism.

PubMed

Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs

2014-08-12

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

The Icebreaker Life Mission to Mars: A Search for Biomolecular Evidence for Life

NASA Technical Reports Server (NTRS)

Mckay, Christopher P.; Stoker, Carol R.; Glass, Brian J.; Dave, Arwen I.; Davila, Alfonso F.; Heldmann, Jennifer L.; Marinova, Margarita M.; Fairen, Alberto G; Quinn, Richard C; Zacny, Kris A.;

The Icebreaker Life Mission to Mars: a search for biomolecular evidence for life.

PubMed

McKay, Christopher P; Stoker, Carol R; Glass, Brian J; Davé, Arwen I; Davila, Alfonso F; Heldmann, Jennifer L; Marinova, Margarita M; Fairen, Alberto G; Quinn, Richard C; Zacny, Kris A; Paulsen, Gale; Smith, Peter H; Parro, Victor; Andersen, Dale T; Hecht, Michael H; Lacelle, Denis; Pollard, Wayne H

2013-04-01

The search for evidence of life on Mars is the primary motivation for the exploration of that planet. The results from previous missions, and the Phoenix mission in particular, indicate that the ice-cemented ground in the north polar plains is likely to be the most recently habitable place that is currently known on Mars. The near-surface ice likely provided adequate water activity during periods of high obliquity, ≈ 5 Myr ago. Carbon dioxide and nitrogen are present in the atmosphere, and nitrates may be present in the soil. Perchlorate in the soil together with iron in basaltic rock provides a possible energy source for life. Furthermore, the presence of organics must once again be considered, as the results of the Viking GCMS are now suspect given the discovery of the thermally reactive perchlorate. Ground ice may provide a way to preserve organic molecules for extended periods of time, especially organic biomarkers. The Mars Icebreaker Life mission focuses on the following science goals: (1) Search for specific biomolecules that would be conclusive evidence of life. (2) Perform a general search for organic molecules in the ground ice. (3) Determine the processes of ground ice formation and the role of liquid water. (4) Understand the mechanical properties of the martian polar ice-cemented soil. (5) Assess the recent habitability of the environment with respect to required elements to support life, energy sources, and possible toxic elements. (6) Compare the elemental composition of the northern plains with midlatitude sites. The Icebreaker Life payload has been designed around the Phoenix spacecraft and is targeted to a site near the Phoenix landing site. However, the Icebreaker payload could be supported on other Mars landing systems. Preliminary studies of the SpaceX Dragon lander show that it could support the Icebreaker payload for a landing either at the Phoenix site or at midlatitudes. Duplicate samples could be cached as a target for possible return by a Mars Sample Return mission. If the samples were shown to contain organic biomarkers, interest in returning them to Earth would be high.

CRITTERS! A Realistic Simulation for Teaching Evolutionary Biology

ERIC Educational Resources Information Center

Latham, Luke G., II; Scully, Erik P.

2008-01-01

Evolutionary processes can be studied in nature and in the laboratory, but time and financial constraints result in few opportunities for undergraduate and high school students to explore the agents of genetic change in populations. One alternative to time consuming and expensive teaching laboratories is the use of computer simulations. We…

Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

PubMed Central

Visser, Marco D.; McMahon, Sean M.; Merow, Cory; Dixon, Philip M.; Record, Sydne; Jongejans, Eelke

2015-01-01

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research. PMID:25811842

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

PubMed Central

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. PMID:24701158

An adaptive evolutionary algorithm for traveling salesman problem with precedence constraints.

PubMed

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Cancer Evolution: Mathematical Models and Computational Inference

PubMed Central

Beerenwinkel, Niko; Schwarz, Roland F.; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. PMID:25293804

Mean-Potential Law in Evolutionary Games

NASA Astrophysics Data System (ADS)

Nałecz-Jawecki, Paweł; Miekisz, Jacek

2018-01-01

The Letter presents a novel way to connect random walks, stochastic differential equations, and evolutionary game theory. We introduce a new concept of a potential function for discrete-space stochastic systems. It is based on a correspondence between one-dimensional stochastic differential equations and random walks, which may be exact not only in the continuous limit but also in finite-state spaces. Our method is useful for computation of fixation probabilities in discrete stochastic dynamical systems with two absorbing states. We apply it to evolutionary games, formulating two simple and intuitive criteria for evolutionary stability of pure Nash equilibria in finite populations. In particular, we show that the 1 /3 law of evolutionary games, introduced by Nowak et al. [Nature, 2004], follows from a more general mean-potential law.

Open Issues in Evolutionary Robotics.

PubMed

Silva, Fernando; Duarte, Miguel; Correia, Luís; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

Toward a unifying framework for evolutionary processes.

PubMed

Paixão, Tiago; Badkobeh, Golnaz; Barton, Nick; Çörüş, Doğan; Dang, Duc-Cuong; Friedrich, Tobias; Lehre, Per Kristian; Sudholt, Dirk; Sutton, Andrew M; Trubenová, Barbora

2015-10-21

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Computation of direct and inverse mutations with the SEGM web server (Stochastic Evolution of Genetic Motifs): an application to splice sites of human genome introns.

PubMed

Benard, Emmanuel; Michel, Christian J

2009-08-01

We present here the SEGM web server (Stochastic Evolution of Genetic Motifs) in order to study the evolution of genetic motifs both in the direct evolutionary sense (past-present) and in the inverse evolutionary sense (present-past). The genetic motifs studied can be nucleotides, dinucleotides and trinucleotides. As an example of an application of SEGM and to understand its functionalities, we give an analysis of inverse mutations of splice sites of human genome introns. SEGM is freely accessible at http://lsiit-bioinfo.u-strasbg.fr:8080/webMathematica/SEGM/SEGM.html directly or by the web site http://dpt-info.u-strasbg.fr/~michel/. To our knowledge, this SEGM web server is to date the only computational biology software in this evolutionary approach.

Biology Needs Evolutionary Software Tools: Let’s Build Them Right

PubMed Central

Team, Galaxy; Goecks, Jeremy; Taylor, James

2018-01-01

Abstract Research in population genetics and evolutionary biology has always provided a computational backbone for life sciences as a whole. Today evolutionary and population biology reasoning are essential for interpretation of large complex datasets that are characteristic of all domains of today’s life sciences ranging from cancer biology to microbial ecology. This situation makes algorithms and software tools developed by our community more important than ever before. This means that we, developers of software tool for molecular evolutionary analyses, now have a shared responsibility to make these tools accessible using modern technological developments as well as provide adequate documentation and training. PMID:29688462

Radiation hard programmable delay line for LHCb calorimeter upgrade

NASA Astrophysics Data System (ADS)

Mauricio, J.; Gascón, D.; Vilasís, X.; Picatoste, E.; Machefert, F.; Lefrancois, J.; Duarte, O.; Beigbeder, C.

2014-01-01

This paper describes the implementation of a SPI-programmable clock delay chip based on a Delay Locked Loop (DLL) in order to shift the phase of the LHC clock (25 ns) in steps of 1ns, with less than 5 ps jitter and 23 ps of DNL. The delay lines will be integrated into ICECAL, the LHCb calorimeter front-end analog signal processing ASIC in the near future. The stringent noise requirements on the ASIC imply minimizing the noise contribution of digital components. This is accomplished by implementing the DLL in differential mode. To achieve the required radiation tolerance several techniques are applied: double guard rings between PMOS and NMOS transistors as well as glitch suppressors and TMR Registers. This 5.7 mm2 chip has been implemented in CMOS 0.35 μm technology.

Evolving Better Cars: Teaching Evolution by Natural Selection with a Digital Inquiry Activity

ERIC Educational Resources Information Center

Royer, Anne M.; Schultheis, Elizabeth H.

2014-01-01

Evolutionary experiments are usually difficult to perform in the classroom because of the large sizes and long timescales of experiments testing evolutionary hypotheses. Computer applications give students a window to observe evolution in action, allowing them to gain comfort with the process of natural selection and facilitating inquiry…

Memetic Algorithms, Domain Knowledge, and Financial Investing

ERIC Educational Resources Information Center

Du, Jie

2012-01-01

While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial…

Bipartite graphs as models of population structures in evolutionary multiplayer games.

PubMed

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, "games on graphs" study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner's dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner's dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

Resistance and relatedness on an evolutionary graph

PubMed Central

Maciejewski, Wes

2012-01-01

When investigating evolution in structured populations, it is often convenient to consider the population as an evolutionary graph—individuals as nodes, and whom they may act with as edges. There has, in recent years, been a surge of interest in evolutionary graphs, especially in the study of the evolution of social behaviours. An inclusive fitness framework is best suited for this type of study. A central requirement for an inclusive fitness analysis is an expression for the genetic similarity between individuals residing on the graph. This has been a major hindrance for work in this area as highly technical mathematics are often required. Here, I derive a result that links genetic relatedness between haploid individuals on an evolutionary graph to the resistance between vertices on a corresponding electrical network. An example that demonstrates the potential computational advantage of this result over contemporary approaches is provided. This result offers more, however, to the study of population genetics than strictly computationally efficient methods. By establishing a link between gene transfer and electric circuit theory, conceptualizations of the latter can enhance understanding of the former. PMID:21849384

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Mean-Potential Law in Evolutionary Games.

PubMed

Nałęcz-Jawecki, Paweł; Miękisz, Jacek

2018-01-12

The Letter presents a novel way to connect random walks, stochastic differential equations, and evolutionary game theory. We introduce a new concept of a potential function for discrete-space stochastic systems. It is based on a correspondence between one-dimensional stochastic differential equations and random walks, which may be exact not only in the continuous limit but also in finite-state spaces. Our method is useful for computation of fixation probabilities in discrete stochastic dynamical systems with two absorbing states. We apply it to evolutionary games, formulating two simple and intuitive criteria for evolutionary stability of pure Nash equilibria in finite populations. In particular, we show that the 1/3 law of evolutionary games, introduced by Nowak et al. [Nature, 2004], follows from a more general mean-potential law.

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

The Evolution of Biological Complexity in Digital Organisms

NASA Astrophysics Data System (ADS)

Ofria, Charles

2013-03-01

When Darwin first proposed his theory of evolution by natural selection, he realized that it had a problem explaining the origins of traits of ``extreme perfection and complication'' such as the vertebrate eye. Critics of Darwin's theory have latched onto this perceived flaw as a proof that Darwinian evolution is impossible. In anticipation of this issue, Darwin described the perfect data needed to understand this process, but lamented that such data are ``scarcely ever possible'' to obtain. In this talk, I will discuss research where we use populations of digital organisms (self-replicating and evolving computer programs) to elucidate the genetic and evolutionary processes by which new, highly-complex traits arise, drawing inspiration directly from Darwin's wistful thinking and hypotheses. During the process of evolution in these fully-transparent computational environments we can measure the incorporation of new information into the genome, a process akin to a natural Maxwell's Demon, and identify the original source of any such information. We show that, as Darwin predicted, much of the information used to encode a complex trait was already in the genome as part of simpler evolved traits, and that many routes must be possible for a new complex trait to have a high probability of successfully evolving. In even more extreme examples of the evolution of complexity, we are now using these same principles to examine the evolutionary dynamics the drive major transitions in evolution; that is transitions to higher-levels of organization, which are some of the most complex evolutionary events to occur in nature. Finally, I will explore some of the implications of this research to other aspects of evolutionary biology and as well as ways that these evolutionary principles can be applied toward solving computational and engineering problems.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

PubMed

Mrowinski, Maciej J; Fronczak, Piotr; Fronczak, Agata; Ausloos, Marcel; Nedic, Olgica

2017-01-01

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors' workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

Evolutionary fuzzy modeling human diagnostic decisions.

PubMed

Peña-Reyes, Carlos Andrés

2004-05-01

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

PubMed Central

Bryson, David M.; Ofria, Charles

2013-01-01

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges. PMID:24376669

A framework for evolutionary systems biology

PubMed Central

Loewe, Laurence

2009-01-01

Background Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects. Results Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism. Conclusion EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications. PMID:19239699

A Bright Future for Evolutionary Methods in Drug Design.

PubMed

Le, Tu C; Winkler, David A

2015-08-01

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An evolutionary algorithm that constructs recurrent neural networks.

PubMed

Angeline, P J; Saunders, G M; Pollack, J B

1994-01-01

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.

EvoluZion: A Computer Simulator for Teaching Genetic and Evolutionary Concepts

ERIC Educational Resources Information Center

Zurita, Adolfo R.

2017-01-01

EvoluZion is a forward-in-time genetic simulator developed in Java and designed to perform real time simulations on the evolutionary history of virtual organisms. These model organisms harbour a set of 13 genes that codify an equal number of phenotypic features. These genes change randomly during replication, and mutant genes can have null,…

Generative Representations for Computer-Automated Evolutionary Design

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2006-01-01

With the increasing computational power of computers, software design systems are progressing from being tools for architects and designers to express their ideas to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design systems is the representation with which they encode designs. If the representation cannot encode a certain design, then the design system cannot produce it. To be able to produce new types of designs, and not just optimize pre-defined parameterizations, evolutionary design systems must use generative representations. Generative representations are assembly procedures, or algorithms, for constructing a design thereby allowing for truly novel design solutions to be encoded. In addition, by enabling modularity, regularity and hierarchy, the level of sophistication that can be evolved is increased. We demonstrate the advantages of generative representations on two different design domains: the evolution of spacecraft antennas and the evolution of 3D objects.

Cancer evolution: mathematical models and computational inference.

PubMed

Beerenwinkel, Niko; Schwarz, Roland F; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

Computational evolution: taking liberties.

PubMed

Correia, Luís

2010-09-01

Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

Understanding sequence similarity and framework analysis between centromere proteins using computational biology.

PubMed

Doss, C George Priya; Chakrabarty, Chiranjib; Debajyoti, C; Debottam, S

2014-11-01

Certain mysteries pointing toward their recruitment pathways, cell cycle regulation mechanisms, spindle checkpoint assembly, and chromosome segregation process are considered the centre of attraction in cancer research. In modern times, with the established databases, ranges of computational platforms have provided a platform to examine almost all the physiological and biochemical evidences in disease-associated phenotypes. Using existing computational methods, we have utilized the amino acid residues to understand the similarity within the evolutionary variance of different associated centromere proteins. This study related to sequence similarity, protein-protein networking, co-expression analysis, and evolutionary trajectory of centromere proteins will speed up the understanding about centromere biology and will create a road map for upcoming researchers who are initiating their work of clinical sequencing using centromere proteins.

Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games

PubMed Central

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures. PMID:22970237

The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking

NASA Astrophysics Data System (ADS)

Ma, Zhanshan (Sam); Krings, Axel W.; Hung, Chih-Cheng

Communication is a fundamental function of life, and it exists in almost all living things: from single-cell bacteria to human beings. Communication, together with competition and cooperation,arethree fundamental processes in nature. Computer scientists are familiar with the study of competition or 'struggle for life' through Darwin's evolutionary theory, or even evolutionary computing. They may be equally familiar with the study of cooperation or altruism through the Prisoner's Dilemma (PD) game. However, they are likely to be less familiar with the theory of animal communication. The objective of this article is three-fold: (i) To suggest that the study of animal communication, especially the honesty (reliability) of animal communication, in which some significant advances in behavioral biology have been achieved in the last three decades, should be on the verge to spawn important cross-disciplinary research similar to that generated by the study of cooperation with the PD game. One of the far-reaching advances in the field is marked by the publication of "The Handicap Principle: a Missing Piece of Darwin's Puzzle" by Zahavi (1997). The 'Handicap' principle [34][35], which states that communication signals must be costly in some proper way to be reliable (honest), is best elucidated with evolutionary games, e.g., Sir Philip Sidney (SPS) game [23]. Accordingly, we suggest that the Handicap principle may serve as a fundamental paradigm for trust research in computer science. (ii) To suggest to computer scientists that their expertise in modeling computer networks may help behavioral biologists in their study of the reliability of animal communication networks. This is largely due to the historical reason that, until the last decade, animal communication was studied with the dyadic paradigm (sender-receiver) rather than with the network paradigm. (iii) To pose several open questions, the answers to which may bear some refreshing insights to trust research in computer science, especially secure and resilient computing, the semantic web, and social networking. One important thread unifying the three aspects is the evolutionary game theory modeling or its extensions with survival analysis and agreement algorithms [19][20], which offer powerful game models for describing time-, space-, and covariate-dependent frailty (uncertainty and vulnerability) and deception (honesty).

Computer-Automated Evolution of Spacecraft X-Band Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Homby, Gregory S.; Linden, Derek S.

2010-01-01

A document discusses the use of computer- aided evolution in arriving at a design for X-band communication antennas for NASA s three Space Technology 5 (ST5) satellites, which were launched on March 22, 2006. Two evolutionary algorithms, incorporating different representations of the antenna design and different fitness functions, were used to automatically design and optimize an X-band antenna design. A set of antenna designs satisfying initial ST5 mission requirements was evolved by use these algorithms. The two best antennas - one from each evolutionary algorithm - were built. During flight-qualification testing of these antennas, the mission requirements were changed. After minimal changes in the evolutionary algorithms - mostly in the fitness functions - new antenna designs satisfying the changed mission requirements were evolved and within one month of this change, two new antennas were designed and prototypes of the antennas were built and tested. One of these newly evolved antennas was approved for deployment on the ST5 mission, and flight-qualified versions of this design were built and installed on the spacecraft. At the time of writing the document, these antennas were the first computer-evolved hardware in outer space.

Optimizing a reconfigurable material via evolutionary computation

NASA Astrophysics Data System (ADS)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Evaluation of Generation Alternation Models in Evolutionary Robotics

NASA Astrophysics Data System (ADS)

Oiso, Masashi; Matsumura, Yoshiyuki; Yasuda, Toshiyuki; Ohkura, Kazuhiro

For efficient implementation of Evolutionary Algorithms (EA) to a desktop grid computing environment, we propose a new generation alternation model called Grid-Oriented-Deletion (GOD) based on comparison with the conventional techniques. In previous research, generation alternation models are generally evaluated by using test functions. However, their exploration performance on the real problems such as Evolutionary Robotics (ER) has not been made very clear yet. Therefore we investigate the relationship between the exploration performance of EA on an ER problem and its generation alternation model. We applied four generation alternation models to the Evolutionary Multi-Robotics (EMR), which is the package-pushing problem to investigate their exploration performance. The results show that GOD is more effective than the other conventional models.

Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

Nanotube Heterojunctions and Endo-Fullerenes for Nanoelectronics

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Menon, M.; Andriotis, Antonis; Cho, K.; Park, Jun; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Topics discussed include: (1) Light-Weight Multi-Functional Materials: Nanomechanics; Nanotubes and Composites; Thermal/Chemical/Electrical Characterization; (2) Biomimetic/Revolutionary Concepts: Evolutionary Computing and Sensing; Self-Heating Materials; (3) Central Computing System: Molecular Electronics; Materials for Quantum Bits; and (4) Molecular Machines.

Vision 2010: The Future of Higher Education Business and Learning Applications

ERIC Educational Resources Information Center

Carey, Patrick; Gleason, Bernard

2006-01-01

The global software industry is in the midst of a major evolutionary shift--one based on open computing--and this trend, like many transformative trends in technology, is being led by the IT staffs and academic computing faculty of the higher education industry. The elements of this open computing approach are open source, open standards, open…

Multi-Objective UAV Mission Planning Using Evolutionary Computation

DTIC Science & Technology

2008-03-01

on a Solution Space. . . . . . . . . . . . . . . . . . . . 41 4.3. Crowding distance calculation. Dark points are non-dominated solutions. [14...SPEA2 was devel- oped by Zitzler [64] as an improvement to the original SPEA algorithm [65]. SPEA2 Figure 4.3: Crowding distance calculation. Dark ...thesis, Los Angeles, CA, USA, 2003. Adviser-Maja J. Mataric . 114 21. Homberger, Joerg and Hermann Gehring. “Two Evolutionary Metaheuristics for the

An Evolutionary Algorithm to Generate Ellipsoid Detectors for Negative Selection

DTIC Science & Technology

2005-03-21

of Congress on Evolutionary Computation. Honolulu,. 58. Lamont, Gary B., Robert E. Marmelstein, and David A. Van Veldhuizen . A Distributed Architecture...antibody and an antigen is a function of several processes including electrostatic interactions, hydrogen bonding, van der Waals interaction, and others [20...Kelly, Patrick M., Don R. Hush, and James M. White. “An Adaptive Algorithm for Modifying Hyperellipsoidal Decision Surfaces”. Journal of Artificial

Deposition, Accumulation, and Alteration of Cl(-), NO3(-), ClO4(-) and ClO3(-) Salts in a Hyper-Arid Polar Environment: Mass Balance and Isotopic Constraints

NASA Technical Reports Server (NTRS)

Jackson, Andrew; Davila, Alfonso F.; Boehlke, J. K.; Sturchio, Neil C.; Sevanthi, Ritesh; Estrada, Nubia; Brundrette, Megan; Lacell, Denis; McKay, Christopher P.; Poghosyan, Armen;

Deposition, accumulation, and alteration of Cl-, NO3-, ClO4- and ClO3- salts in a hyper-arid polar environment: Mass balance and isotopic constraints

NASA Astrophysics Data System (ADS)

Jackson, Andrew; Davila, Alfonso F.; Böhlke, John Karl; Sturchio, Neil C.; Sevanthi, Ritesh; Estrada, Nubia; Brundrett, Maeghan; Lacelle, Denis; McKay, Christopher P.; Poghosyan, Armen; Pollard, Wayne; Zacny, Kris

2016-06-01

The salt fraction in permafrost soils/sediments of the McMurdo Dry Valleys (MDV) of Antarctica can be used as a proxy for cold desert geochemical processes and paleoclimate reconstruction. Previous analyses of the salt fraction in MDV permafrost soils have largely been conducted in coastal regions where permafrost soils are variably affected by aqueous processes and mixed inputs from marine and stratospheric sources. We expand upon this work by evaluating permafrost soil/sediments in University Valley, located in the ultraxerous zone where both liquid water transport and marine influences are minimal. We determined the abundances of Cl-, NO3-, ClO4- and ClO3- in dry and ice-cemented soil/sediments, snow and glacier ice, and also characterized Cl- and NO3- isotopically. The data are not consistent with salt deposition in a sublimation till, nor with nuclear weapon testing fall-out, and instead point to a dominantly stratospheric source and to varying degrees of post depositional transformation depending on the substrate, from minimal alteration in bare soils to significant alteration (photodegradation and/or volatilization) in snow and glacier ice. Ionic abundances in the dry permafrost layer indicate limited vertical transport under the current climate conditions, likely due to percolation of snowmelt. Subtle changes in ClO4-/NO3- ratios and NO3- isotopic composition with depth and location may reflect both transport related fractionation and depositional history. Low molar ratios of ClO3-/ClO4- in surface soils compared to deposition and other arid systems suggest significant post depositional loss of ClO3-, possibly due to reduction by iron minerals, which may have important implications for oxy-chlorine species on Mars. Salt accumulation varies with distance along the valley and apparent accumulation times based on multiple methods range from ∼10 to 30 kyr near the glacier to 70-200 kyr near the valley mouth. The relatively young age of the salts and relatively low and homogeneous anion concentrations in the ice-cemented sediments point to either a mechanism of recent salt removal, or to relatively modern permafrost soils (<1 million years). Together, our results show that near surface salts in University Valley serve as an end-member of stratospheric sources not subject to biological processes or extensive remobilization.

Evolutionary Models for Simple Biosystems

NASA Astrophysics Data System (ADS)

Bagnoli, Franco

The concept of evolutionary development of structures constituted a real revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

PubMed Central

Fronczak, Piotr; Fronczak, Agata; Ausloos, Marcel; Nedic, Olgica

2017-01-01

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors’ workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems. PMID:28931033

Marr's levels and the minimalist program.

PubMed

Johnson, Mark

2017-02-01

A simple change to a cognitive system at Marr's computational level may entail complex changes at the other levels of description of the system. The implementational level complexity of a change, rather than its computational level complexity, may be more closely related to the plausibility of a discrete evolutionary event causing that change. Thus the formal complexity of a change at the computational level may not be a good guide to the plausibility of an evolutionary event introducing that change. For example, while the Minimalist Program's Merge is a simple formal operation (Berwick & Chomsky, 2016), the computational mechanisms required to implement the language it generates (e.g., to parse the language) may be considerably more complex. This has implications for the theory of grammar: theories of grammar which involve several kinds of syntactic operations may be no less evolutionarily plausible than a theory of grammar that involves only one. A deeper understanding of human language at the algorithmic and implementational levels could strengthen Minimalist Program's account of the evolution of language.

Soft computing approach to 3D lung nodule segmentation in CT.

PubMed

Badura, P; Pietka, E

2014-10-01

This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database. Copyright © 2014 Elsevier Ltd. All rights reserved.

Applications of genetic programming in cancer research.

PubMed

Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

2009-02-01

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

PubMed

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-04-01

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.

Computability, Gödel's incompleteness theorem, and an inherent limit on the predictability of evolution

PubMed Central

Day, Troy

2012-01-01

The process of evolutionary diversification unfolds in a vast genotypic space of potential outcomes. During the past century, there have been remarkable advances in the development of theory for this diversification, and the theory's success rests, in part, on the scope of its applicability. A great deal of this theory focuses on a relatively small subset of the space of potential genotypes, chosen largely based on historical or contemporary patterns, and then predicts the evolutionary dynamics within this pre-defined set. To what extent can such an approach be pushed to a broader perspective that accounts for the potential open-endedness of evolutionary diversification? There have been a number of significant theoretical developments along these lines but the question of how far such theory can be pushed has not been addressed. Here a theorem is proven demonstrating that, because of the digital nature of inheritance, there are inherent limits on the kinds of questions that can be answered using such an approach. In particular, even in extremely simple evolutionary systems, a complete theory accounting for the potential open-endedness of evolution is unattainable unless evolution is progressive. The theorem is closely related to Gödel's incompleteness theorem, and to the halting problem from computability theory. PMID:21849390

Framework for computationally efficient optimal irrigation scheduling using ant colony optimization

USDA-ARS?s Scientific Manuscript database

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decisi...

Pervasive Computing and Communication Technologies for U-Learning

ERIC Educational Resources Information Center

Park, Young C.

2014-01-01

The development of digital information transfer, storage and communication methods influences a significant effect on education. The assimilation of pervasive computing and communication technologies marks another great step forward, with Ubiquitous Learning (U-learning) emerging for next generation learners. In the evolutionary view the 5G (or…

Langley's CSI evolutionary model: Phase O

NASA Technical Reports Server (NTRS)

Belvin, W. Keith; Elliott, Kenny B.; Horta, Lucas G.; Bailey, Jim P.; Bruner, Anne M.; Sulla, Jeffrey L.; Won, John; Ugoletti, Roberto M.

1991-01-01

A testbed for the development of Controls Structures Interaction (CSI) technology to improve space science platform pointing is described. The evolutionary nature of the testbed will permit the study of global line-of-sight pointing in phases 0 and 1, whereas, multipayload pointing systems will be studied beginning with phase 2. The design, capabilities, and typical dynamic behavior of the phase 0 version of the CSI evolutionary model (CEM) is documented for investigator both internal and external to NASA. The model description includes line-of-sight pointing measurement, testbed structure, actuators, sensors, and real time computers, as well as finite element and state space models of major components.

Efficiency of the neighbor-joining method in reconstructing deep and shallow evolutionary relationships in large phylogenies.

PubMed

Kumar, S; Gadagkar, S R

2000-12-01

The neighbor-joining (NJ) method is widely used in reconstructing large phylogenies because of its computational speed and the high accuracy in phylogenetic inference as revealed in computer simulation studies. However, most computer simulation studies have quantified the overall performance of the NJ method in terms of the percentage of branches inferred correctly or the percentage of replications in which the correct tree is recovered. We have examined other aspects of its performance, such as the relative efficiency in correctly reconstructing shallow (close to the external branches of the tree) and deep branches in large phylogenies; the contribution of zero-length branches to topological errors in the inferred trees; and the influence of increasing the tree size (number of sequences), evolutionary rate, and sequence length on the efficiency of the NJ method. Results show that the correct reconstruction of deep branches is no more difficult than that of shallower branches. The presence of zero-length branches in realized trees contributes significantly to the overall error observed in the NJ tree, especially in large phylogenies or slowly evolving genes. Furthermore, the tree size does not influence the efficiency of NJ in reconstructing shallow and deep branches in our simulation study, in which the evolutionary process is assumed to be homogeneous in all lineages.

Spore: Spawning Evolutionary Misconceptions?

NASA Astrophysics Data System (ADS)

Bean, Thomas E.; Sinatra, Gale M.; Schrader, P. G.

2010-10-01

The use of computer simulations as educational tools may afford the means to develop understanding of evolution as a natural, emergent, and decentralized process. However, special consideration of developmental constraints on learning may be necessary when using these technologies. Specifically, the essentialist (biological forms possess an immutable essence), teleological (assignment of purpose to living things and/or parts of living things that may not be purposeful), and intentionality (assumption that events are caused by an intelligent agent) biases may be reinforced through the use of computer simulations, rather than addressed with instruction. We examine the video game Spore for its depiction of evolutionary content and its potential to reinforce these cognitive biases. In particular, we discuss three pedagogical strategies to mitigate weaknesses of Spore and other computer simulations: directly targeting misconceptions through refutational approaches, targeting specific principles of scientific inquiry, and directly addressing issues related to models as cognitive tools.

Parallel Evolutionary Optimization for Neuromorphic Network Training

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schuman, Catherine D; Disney, Adam; Singh, Susheela

One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impactmore » the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.« less

A computer lab exploring evolutionary aspects of chromatin structure and dynamics for an undergraduate chromatin course*.

PubMed

Eirín-López, José M

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a vast body of knowledge to the classroom. With this aim, the present work describes a multidisciplinary computer lab designed to introduce undergraduate students to the dynamic nature of chromatin, within the context of the one semester course "Chromatin: Structure, Function and Evolution." This exercise is organized in three parts including (a) molecular evolutionary biology of histone families (using the H1 family as example), (b) histone structure and variation across different animal groups, and (c) effect of histone diversity on nucleosome structure and chromatin dynamics. By using freely available bioinformatic tools that can be run on common computers, the concept of chromatin dynamics is interactively illustrated from a comparative/evolutionary perspective. At the end of this computer lab, students are able to translate the bioinformatic information into a biochemical context in which the relevance of histone primary structure on chromatin dynamics is exposed. During the last 8 years this exercise has proven to be a powerful approach for teaching chromatin structure and dynamics, allowing students a higher degree of independence during the processes of learning and self-assessment. Copyright © 2013 International Union of Biochemistry and Molecular Biology, Inc.

Inquiry-Based Learning of Molecular Phylogenetics

ERIC Educational Resources Information Center

Campo, Daniel; Garcia-Vazquez, Eva

2008-01-01

Reconstructing phylogenies from nucleotide sequences is a challenge for students because it strongly depends on evolutionary models and computer tools that are frequently updated. We present here an inquiry-based course aimed at learning how to trace a phylogeny based on sequences existing in public databases. Computer tools are freely available…

International Conference on Artificial Immune Systems (1st) ICARIS 2002, held on 9, 10, and 11 September 2002

DTIC Science & Technology

2002-03-07

Michalewicz, Eds., Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics, Bristol (UK), 2000. [3] David A. Van Veldhuizen ...2000. [4] Carlos A. Coello Coello, David A. Van Veldhuizen , and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer...Academic Publishers, 233 Spring St., New York, NY 10013, 2002. [5] David A. Van Veldhuizen , Multiobjective Evolution- ary Algorithms: Classifications

Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

PubMed

Wright, Cameron H G; Barrett, Steven F; Pack, Daniel J

2005-01-01

We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.

Automated Antenna Design with Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.; Globus, Al; Linden, Derek S.; Lohn, Jason D.

2006-01-01

Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to constrain the evolutionary design to a monopole wire antenna. The results of the runs produced requirements-compliant antennas that were subsequently fabricated and tested. The evolved antenna has a number of advantages with regard to power consumption, fabrication time and complexity, and performance. Lower power requirements result from achieving high gain across a wider range of elevation angles, thus allowing a broader range of angles over which maximum data throughput can be achieved. Since the evolved antenna does not require a phasing circuit, less design and fabrication work is required. In terms of overall work, the evolved antenna required approximately three person-months to design and fabricate whereas the conventional antenna required about five. Furthermore, when the mission was modified and new orbital parameters selected, a redesign of the antenna to new requirements was required. The evolutionary system was rapidly modified and a new antenna evolved in a few weeks. The evolved antenna was shown to be compliant to the ST5 mission requirements. It has an unusual organic looking structure, one that expert antenna designers would not likely produce. This antenna has been tested, baselined and is scheduled to fly this year. In addition to the ST5 antenna, our laboratory has evolved an S-band phased array antenna element design that meets the requirements for NASA's TDRS-C communications satellite scheduled for launch early next decade. A combination of fairly broad bandwidth, high efficiency and circular polarization at high gain made for another challenging design problem. We chose to constrain the evolutionary design to a crossed-element Yagi antenna. The specification called for two types of elements, one for receive only and one for transmit/receive. We were able to evolve a single element design that meets both specifications thereby simplifying the antenna and reducing testing and integration costs. The highest performance antenna found using a getic algorithm and stochastic hill-climbing has been fabricated and tested. Laboratory results correspond well with simulation. Aerospace component design is an expensive and important step in space development. Evolutionary design can make a significant contribution wherever sufficiently fast, accurate and capable software simulators are available. We have demonstrated successful real-world design in the spacecraft antenna domain; and there is good reason to believe that these results could be replicated in other design spaces.

Hybrid Genetic Agorithms and Line Search Method for Industrial Production Planning with Non-Linear Fitness Function

NASA Astrophysics Data System (ADS)

Vasant, Pandian; Barsoum, Nader

2008-10-01

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

Hardware platforms for MEMS gyroscope tuning based on evolutionary computation using open-loop and closed -loop frequency response

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Ferguson, Michael I.; Fink, Wolfgang; Oks, Boris; Peay, Chris; Terrile, Richard; Cheng, Yen; Kim, Dennis; MacDonald, Eric; Foor, David

2005-01-01

We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation. We also report on the development of a hardware platform for integrated tuning and closed loop operation of MEMS gyroscopes. The control of this device is implemented through a digital design on a Field Programmable Gate Array (FPGA). The hardware platform easily transitions to an embedded solution that allows for the miniaturization of the system to a single chip.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

PubMed Central

Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour. PMID:28045964

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

NASA Astrophysics Data System (ADS)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

PubMed

Demšar, Jure; Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

The tangled bank of amino acids

PubMed Central

Pollock, David D.

2016-01-01

Abstract The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. PMID:27028523

Evolutionary Optimization of a Geometrically Refined Truss

NASA Technical Reports Server (NTRS)

Hull, P. V.; Tinker, M. L.; Dozier, G. V.

2007-01-01

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This Technical Publication (TP) presents a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: genetic algorithms and differential evolution to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this TP, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

On the numerical treatment of selected oscillatory evolutionary problems

NASA Astrophysics Data System (ADS)

Cardone, Angelamaria; Conte, Dajana; D'Ambrosio, Raffaele; Paternoster, Beatrice

2017-07-01

We focus on evolutionary problems whose qualitative behaviour is known a-priori and exploited in order to provide efficient and accurate numerical schemes. For classical numerical methods, depending on constant coefficients, the required computational effort could be quite heavy, due to the necessary employ of very small stepsizes needed to accurately reproduce the qualitative behaviour of the solution. In these situations, it may be convenient to use special purpose formulae, i.e. non-polynomially fitted formulae on basis functions adapted to the problem (see [16, 17] and references therein). We show examples of special purpose strategies to solve two families of evolutionary problems exhibiting periodic solutions, i.e. partial differential equations and Volterra integral equations.

Human evolutionary genomics: ethical and interpretive issues.

PubMed

Vitti, Joseph J; Cho, Mildred K; Tishkoff, Sarah A; Sabeti, Pardis C

2012-03-01

Genome-wide computational studies can now identify targets of natural selection. The unique information about humans these studies reveal, and the media attention they attract, indicate the need for caution and precision in communicating results. This need is exacerbated by ways in which evolutionary and genetic considerations have been misapplied to support discriminatory policies, by persistent misconceptions of these fields and by the social sensitivity surrounding discussions of racial ancestry. We discuss the foundations, accomplishments and future directions of human evolutionary genomics, attending to ways in which the interpretation of good science can go awry, and offer suggestions for researchers to prevent misapplication of their work. Copyright © 2011 Elsevier Ltd. All rights reserved.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation.

PubMed

Cornuet, Jean-Marie; Santos, Filipe; Beaumont, Mark A; Robert, Christian P; Marin, Jean-Michel; Balding, David J; Guillemaud, Thomas; Estoup, Arnaud

2008-12-01

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

A program to compute the soft Robinson-Foulds distance between phylogenetic networks.

PubMed

Lu, Bingxin; Zhang, Louxin; Leong, Hon Wai

2017-03-14

Over the past two decades, phylogenetic networks have been studied to model reticulate evolutionary events. The relationships among phylogenetic networks, phylogenetic trees and clusters serve as the basis for reconstruction and comparison of phylogenetic networks. To understand these relationships, two problems are raised: the tree containment problem, which asks whether a phylogenetic tree is displayed in a phylogenetic network, and the cluster containment problem, which asks whether a cluster is represented at a node in a phylogenetic network. Both the problems are NP-complete. A fast exponential-time algorithm for the cluster containment problem on arbitrary networks is developed and implemented in C. The resulting program is further extended into a computer program for fast computation of the Soft Robinson-Foulds distance between phylogenetic networks. Two computer programs are developed for facilitating reconstruction and validation of phylogenetic network models in evolutionary and comparative genomics. Our simulation tests indicated that they are fast enough for use in practice. Additionally, the distribution of the Soft Robinson-Foulds distance between phylogenetic networks is demonstrated to be unlikely normal by our simulation data.

Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

PubMed

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

Replaying evolutionary transitions from the dental fossil record

PubMed Central

Harjunmaa, Enni; Seidel, Kerstin; Häkkinen, Teemu; Renvoisé, Elodie; Corfe, Ian J.; Kallonen, Aki; Zhang, Zhao-Qun; Evans, Alistair R.; Mikkola, Marja L.; Salazar-Ciudad, Isaac; Klein, Ophir D.; Jernvall, Jukka

2014-01-01

The evolutionary relationships of extinct species are ascertained primarily through the analysis of morphological characters. Character inter-dependencies can have a substantial effect on evolutionary interpretations, but the developmental underpinnings of character inter-dependence remain obscure because experiments frequently do not provide detailed resolution of morphological characters. Here we show experimentally and computationally how gradual modification of development differentially affects characters in the mouse dentition. We found that intermediate phenotypes could be produced by gradually adding ectodysplasin A (EDA) protein in culture to tooth explants carrying a null mutation in the tooth-patterning gene Eda. By identifying development-based character interdependencies, we show how to predict morphological patterns of teeth among mammalian species. Finally, in vivo inhibition of sonic hedgehog signalling in Eda null teeth enabled us to reproduce characters deep in the rodent ancestry. Taken together, evolutionarily informative transitions can be experimentally reproduced, thereby providing development-based expectations for character state transitions used in evolutionary studies. PMID:25079326

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Incorporating evolutionary processes into population viability models.

PubMed

Pierson, Jennifer C; Beissinger, Steven R; Bragg, Jason G; Coates, David J; Oostermeijer, J Gerard B; Sunnucks, Paul; Schumaker, Nathan H; Trotter, Meredith V; Young, Andrew G

2015-06-01

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence. © 2014 Society for Conservation Biology.

Historical Contingency in Controlled Evolution

NASA Astrophysics Data System (ADS)

Schuster, Peter

2014-12-01

A basic question in evolution is dealing with the nature of an evolutionary memory. At thermodynamic equilibrium, at stable stationary states or other stable attractors the memory on the path leading to the long-time solution is erased, at least in part. Similar arguments hold for unique optima. Optimality in biology is discussed on the basis of microbial metabolism. Biology, on the other hand, is characterized by historical contingency, which has recently become accessible to experimental test in bacterial populations evolving under controlled conditions. Computer simulations give additional insight into the nature of the evolutionary memory, which is ultimately caused by the enormous space of possibilities that is so large that it escapes all attempts of visualization. In essence, this contribution is dealing with two questions of current evolutionary theory: (i) Are organisms operating at optimal performance? and (ii) How is the evolutionary memory built up in populations?

Human vs. Computer Diagnosis of Students' Natural Selection Knowledge: Testing the Efficacy of Text Analytic Software

NASA Astrophysics Data System (ADS)

Nehm, Ross H.; Haertig, Hendrik

2012-02-01

Our study examines the efficacy of Computer Assisted Scoring (CAS) of open-response text relative to expert human scoring within the complex domain of evolutionary biology. Specifically, we explored whether CAS can diagnose the explanatory elements (or Key Concepts) that comprise undergraduate students' explanatory models of natural selection with equal fidelity as expert human scorers in a sample of >1,000 essays. We used SPSS Text Analysis 3.0 to perform our CAS and measure Kappa values (inter-rater reliability) of KC detection (i.e., computer-human rating correspondence). Our first analysis indicated that the text analysis functions (or extraction rules) developed and deployed in SPSSTA to extract individual Key Concepts (KCs) from three different items differing in several surface features (e.g., taxon, trait, type of evolutionary change) produced "substantial" (Kappa 0.61-0.80) or "almost perfect" (0.81-1.00) agreement. The second analysis explored the measurement of human-computer correspondence for KC diversity (the number of different accurate knowledge elements) in the combined sample of all 827 essays. Here we found outstanding correspondence; extraction rules generated using one prompt type are broadly applicable to other evolutionary scenarios (e.g., bacterial resistance, cheetah running speed, etc.). This result is encouraging, as it suggests that the development of new item sets may not necessitate the development of new text analysis rules. Overall, our findings suggest that CAS tools such as SPSS Text Analysis may compensate for some of the intrinsic limitations of currently used multiple-choice Concept Inventories designed to measure student knowledge of natural selection.

Computing the origin and evolution of the ribosome from its structure — Uncovering processes of macromolecular accretion benefiting synthetic biology

PubMed Central

Caetano-Anollés, Gustavo; Caetano-Anollés, Derek

2015-01-01

Accretion occurs pervasively in nature at widely different timeframes. The process also manifests in the evolution of macromolecules. Here we review recent computational and structural biology studies of evolutionary accretion that make use of the ideographic (historical, retrodictive) and nomothetic (universal, predictive) scientific frameworks. Computational studies uncover explicit timelines of accretion of structural parts in molecular repertoires and molecules. Phylogenetic trees of protein structural domains and proteomes and their molecular functions were built from a genomic census of millions of encoded proteins and associated terminal Gene Ontology terms. Trees reveal a ‘metabolic-first’ origin of proteins, the late development of translation, and a patchwork distribution of proteins in biological networks mediated by molecular recruitment. Similarly, the natural history of ancient RNA molecules inferred from trees of molecular substructures built from a census of molecular features shows patchwork-like accretion patterns. Ideographic analyses of ribosomal history uncover the early appearance of structures supporting mRNA decoding and tRNA translocation, the coevolution of ribosomal proteins and RNA, and a first evolutionary transition that brings ribosomal subunits together into a processive protein biosynthetic complex. Nomothetic structural biology studies of tertiary interactions and ancient insertions in rRNA complement these findings, once concentric layering assumptions are removed. Patterns of coaxial helical stacking reveal a frustrated dynamics of outward and inward ribosomal growth possibly mediated by structural grafting. The early rise of the ribosomal ‘turnstile’ suggests an evolutionary transition in natural biological computation. Results make explicit the need to understand processes of molecular growth and information transfer of macromolecules. PMID:27096056

Nearing the cold-arid limits of microbial life in permafrost of an upper dry valley, Antarctica.

PubMed

Goordial, Jacqueline; Davila, Alfonso; Lacelle, Denis; Pollard, Wayne; Marinova, Margarita M; Greer, Charles W; DiRuggiero, Jocelyn; McKay, Christopher P; Whyte, Lyle G

2016-07-01

Some of the coldest and driest permafrost soils on Earth are located in the high-elevation McMurdo Dry Valleys (MDVs) of Antarctica, but little is known about the permafrost microbial communities other than that microorganisms are present in these valleys. Here, we describe the microbiology and habitable conditions of highly unique dry and ice-cemented permafrost in University Valley, one of the coldest and driest regions in the MDVs (1700 m above sea level; mean temperature -23 °C; no degree days above freezing), where the ice in permafrost originates from vapour deposition rather than liquid water. We found that culturable and total microbial biomass in University Valley was extremely low, and microbial activity under ambient conditions was undetectable. Our results contrast with reports from the lower-elevation Dry Valleys and Arctic permafrost soils where active microbial populations are found, suggesting that the combination of severe cold, aridity, oligotrophy of University Valley permafrost soils severely limit microbial activity and survival.

Nearing the cold-arid limits of microbial life in permafrost of an upper dry valley, Antarctica

PubMed Central

Goordial, Jacqueline; Davila, Alfonso; Lacelle, Denis; Pollard, Wayne; Marinova, Margarita M; Greer, Charles W; DiRuggiero, Jocelyn; McKay, Christopher P; Whyte, Lyle G

2016-01-01

Some of the coldest and driest permafrost soils on Earth are located in the high-elevation McMurdo Dry Valleys (MDVs) of Antarctica, but little is known about the permafrost microbial communities other than that microorganisms are present in these valleys. Here, we describe the microbiology and habitable conditions of highly unique dry and ice-cemented permafrost in University Valley, one of the coldest and driest regions in the MDVs (1700 m above sea level; mean temperature −23 °C; no degree days above freezing), where the ice in permafrost originates from vapour deposition rather than liquid water. We found that culturable and total microbial biomass in University Valley was extremely low, and microbial activity under ambient conditions was undetectable. Our results contrast with reports from the lower-elevation Dry Valleys and Arctic permafrost soils where active microbial populations are found, suggesting that the combination of severe cold, aridity, oligotrophy of University Valley permafrost soils severely limit microbial activity and survival. PMID:27323892

Computer-automated evolution of an X-band antenna for NASA's Space Technology 5 mission.

PubMed

Hornby, Gregory S; Lohn, Jason D; Linden, Derek S

2011-01-01

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

Improving Search Properties in Genetic Programming

NASA Technical Reports Server (NTRS)

Janikow, Cezary Z.; DeWeese, Scott

1997-01-01

With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also developed and implemented methods to use additional problem heuristics to fine-tune the searchable space, and to use typing information to further reduce the search space. Additional improvements have been proposed, but they are yet to be explored and implemented.

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) asmore » a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.« less

New Genetics

MedlinePlus

... Century-Old Evolutionary Puzzle Computing Genetics Model Organisms RNA Interference The New Genetics is a science education ... the basics of DNA and its molecular cousin RNA, and new directions in genetic research. The New ...

An Evolutionary Examination of Telemedicine: A Health and Computer-Mediated Communication Perspective

PubMed Central

Breen, Gerald-Mark; Matusitz, Jonathan

2009-01-01

Telemedicine, the use of advanced communication technologies in the healthcare context, has a rich history and a clear evolutionary course. In this paper, the authors identify telemedicine as operationally defined, the services and technologies it comprises, the direction telemedicine has taken, along with its increased acceptance in the healthcare communities. The authors also describe some of the key pitfalls warred with by researchers and activists to advance telemedicine to its full potential and lead to an unobstructed team of technicians to identify telemedicine’s diverse utilities. A discussion and future directions section is included to provide fresh ideas to health communication and computer-mediated scholars wishing to delve into this area and make a difference to enhance public understanding of this field. PMID:20300559

The Chomsky—Place correspondence 1993–1994

PubMed Central

Chomsky, Noam; Place, Ullin T.

2000-01-01

Edited correspondence between Ullin T. Place and Noam Chomsky, which occurred in 1993–1994, is presented. The principal topics are (a) deep versus surface structure; (b) computer modeling of the brain; (c) the evolutionary origins of language; (d) behaviorism; and (e) a dispositional account of language. This correspondence includes Chomsky's denial that he ever characterized deep structure as innate; Chomsky's critique of computer modeling (both traditional and connectionist) of the brain; Place's critique of Chomsky's alleged failure to provide an adequate account of the evolutionary origins of language, and Chomsky's response that such accounts are “pop-Darwinian fairy tales”; and Place's arguments for, and Chomsky's against, the relevance of behaviorism to linguistic theory, especially the relevance of a behavioral approach to language that is buttressed by a dispositional account of sentence construction. PMID:22477211

The Chomsky-Place correspondence 1993-1994.

PubMed

Chomsky, N; Place, U T

2000-01-01

Edited correspondence between Ullin T. Place and Noam Chomsky, which occurred in 1993-1994, is presented. The principal topics are (a) deep versus surface structure; (b) computer modeling of the brain; (c) the evolutionary origins of language; (d) behaviorism; and (e) a dispositional account of language. This correspondence includes Chomsky's denial that he ever characterized deep structure as innate; Chomsky's critique of computer modeling (both traditional and connectionist) of the brain; Place's critique of Chomsky's alleged failure to provide an adequate account of the evolutionary origins of language, and Chomsky's response that such accounts are "pop-Darwinian fairy tales"; and Place's arguments for, and Chomsky's against, the relevance of behaviorism to linguistic theory, especially the relevance of a behavioral approach to language that is buttressed by a dispositional account of sentence construction.

The development of the red giant branch. I - Theoretical evolutionary sequences

NASA Technical Reports Server (NTRS)

Sweigart, Allen V.; Greggio, Laura; Renzini, Alvio

1989-01-01

A grid of 100 evolutionary sequences extending from the zero-age main sequence to the onset of helium burning has been computed for stellar masses between 1.4 and 3.4 solar masses, helium abundances of 0.20 and 0.30, and heavy-element abundances of 0.004, 0.01, and 0.04. Using these computations the transition in the morphology of the red giant branch (RGB) between low-mass stars, which have an extended and luminous first RGB phase prior to helium ignition, and intermediate-mass stars, which do not, is investigated. Extensive tabulations of the numerical results are provided to aid in applying these sequences. The effects of the first dredge-up on the surface helium and CNO abundances of the sequences is discussed.

Toward a method for tracking virus evolutionary trajectory applied to the pandemic H1N1 2009 influenza virus.

PubMed

Squires, R Burke; Pickett, Brett E; Das, Sajal; Scheuermann, Richard H

2014-12-01

In 2009 a novel pandemic H1N1 influenza virus (H1N1pdm09) emerged as the first official influenza pandemic of the 21st century. Early genomic sequence analysis pointed to the swine origin of the virus. Here we report a novel computational approach to determine the evolutionary trajectory of viral sequences that uses data-driven estimations of nucleotide substitution rates to track the gradual accumulation of observed sequence alterations over time. Phylogenetic analysis and multiple sequence alignments show that sequences belonging to the resulting evolutionary trajectory of the H1N1pdm09 lineage exhibit a gradual accumulation of sequence variations and tight temporal correlations in the topological structure of the phylogenetic trees. These results suggest that our evolutionary trajectory analysis (ETA) can more effectively pinpoint the evolutionary history of viruses, including the host and geographical location traversed by each segment, when compared against either BLAST or traditional phylogenetic analysis alone. Copyright © 2014 Elsevier B.V. All rights reserved.

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.

Upon Accounting for the Impact of Isoenzyme Loss, Gene Deletion Costs Anticorrelate with Their Evolutionary Rates.

PubMed

Jacobs, Christopher; Lambourne, Luke; Xia, Yu; Segrè, Daniel

2017-01-01

System-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now" and the same gene's historical importance as evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

PubMed

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Energy and time determine scaling in biological and computer designs

PubMed Central

Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

2016-01-01

Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information. This article is part of the themed issue ‘The major synthetic evolutionary transitions’. PMID:27431524

Nemo: an evolutionary and population genetics programming framework.

PubMed

Guillaume, Frédéric; Rougemont, Jacques

2006-10-15

Nemo is an individual-based, genetically explicit and stochastic population computer program for the simulation of population genetics and life-history trait evolution in a metapopulation context. It comes as both a C++ programming framework and an executable program file. Its object-oriented programming design gives it the flexibility and extensibility needed to implement a large variety of forward-time evolutionary models. It provides developers with abstract models allowing them to implement their own life-history traits and life-cycle events. Nemo offers a large panel of population models, from the Island model to lattice models with demographic or environmental stochasticity and a variety of already implemented traits (deleterious mutations, neutral markers and more), life-cycle events (mating, dispersal, aging, selection, etc.) and output operators for saving data and statistics. It runs on all major computer platforms including parallel computing environments. The source code, binaries and documentation are available under the GNU General Public License at http://nemo2.sourceforge.net.

Energy and time determine scaling in biological and computer designs.

PubMed

Moses, Melanie; Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

2016-08-19

Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy-time minimization principle may govern the design of many complex systems that process energy, materials and information.This article is part of the themed issue 'The major synthetic evolutionary transitions'. © 2016 The Author(s).

On the interconnection of stable protein complexes: inter-complex hubs and their conservation in Saccharomyces cerevisiae and Homo sapiens networks.

PubMed

Guerra, Concettina

2015-01-01

Protein complexes are key molecular entities that perform a variety of essential cellular functions. The connectivity of proteins within a complex has been widely investigated with both experimental and computational techniques. We developed a computational approach to identify and characterise proteins that play a role in interconnecting complexes. We computed a measure of inter-complex centrality, the crossroad index, based on disjoint paths connecting proteins in distinct complexes and identified inter-complex hubs as proteins with a high value of the crossroad index. We applied the approach to a set of stable complexes in Saccharomyces cerevisiae and in Homo sapiens. Just as done for hubs, we evaluated the topological and biological properties of inter-complex hubs addressing the following questions. Do inter-complex hubs tend to be evolutionary conserved? What is the relation between crossroad index and essentiality? We found a good correlation between inter-complex hubs and both evolutionary conservation and essentiality.

Comparative modeling of coevolution in communities of unicellular organisms: adaptability and biodiversity.

PubMed

Lashin, Sergey A; Suslov, Valentin V; Matushkin, Yuri G

2010-06-01

We propose an original program "Evolutionary constructor" that is capable of computationally efficient modeling of both population-genetic and ecological problems, combining these directions in one model of required detail level. We also present results of comparative modeling of stability, adaptability and biodiversity dynamics in populations of unicellular haploid organisms which form symbiotic ecosystems. The advantages and disadvantages of two evolutionary strategies of biota formation--a few generalists' taxa-based biota formation and biodiversity-based biota formation--are discussed.

Rapid, Value-based, Evolutionary Acquisition and Its Application to a USMC Tactical Service Oriented Architecture

DTIC Science & Technology

2009-06-01

Availability C2PC Command and Control Personal Computer CAS Close Air Support CCA Clinger-Cohen Act CDR Critical Design Review CJCSI Chairman of the Joint... kids , Jackie and Anna and my future boy whose name is TBD, I think my time at NPS has made me a better person and hopefully a better father. Thank... can the USMC apply the essential principles of rapid, value-based, evolutionary acquisition to the development and procurement of a TSOA? 4 THIS

Laboratory evolution of protein conformational dynamics.

PubMed

Campbell, Eleanor C; Correy, Galen J; Mabbitt, Peter D; Buckle, Ashley M; Tokuriki, Nobuhiko; Jackson, Colin J

2017-11-08

This review focuses on recent work that has begun to establish specific functional roles for protein conformational dynamics, specifically how the conformational landscapes that proteins can sample can evolve under laboratory based evolutionary selection. We discuss recent technical advances in computational and biophysical chemistry, which have provided us with new ways to dissect evolutionary processes. Finally, we offer some perspectives on the emerging view of conformational dynamics and evolution, and the challenges that we face in rationally engineering conformational dynamics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Human vs. Computer Diagnosis of Students' Natural Selection Knowledge: Testing the Efficacy of Text Analytic Software

ERIC Educational Resources Information Center

Nehm, Ross H.; Haertig, Hendrik

2012-01-01

Our study examines the efficacy of Computer Assisted Scoring (CAS) of open-response text relative to expert human scoring within the complex domain of evolutionary biology. Specifically, we explored whether CAS can diagnose the explanatory elements (or Key Concepts) that comprise undergraduate students' explanatory models of natural selection with…

The Future with Cryogenic Fluid Dynamics

NASA Astrophysics Data System (ADS)

Scurlock, R. G.

The applications of cryogenic systems have expanded over the past 50 years into many areas of our lives. During this time, the impact of the common features of Cryogenic Fluid Dynamics, CryoFD, on the economic design of these cryogenic systems, has grown out of a long series of experimental studies carried out by teams of postgraduate students at Southampton University.These studies have sought to understand the heat transfer and convective behavior of cryogenic liquids and vapors, but they have only skimmed over the many findings made, on the strong convective motions of fluids at low temperatures. The convection takes place in temperature gradients up to 10,000 K per meter, and density gradients of 1000% per meter and more, with rapid temperature and spatially dependent changes in physical properties like viscosity and surface tension, making software development and empirical correlations almost impossible to achieve. These temperature and density gradients are far larger than those met in other convecting systems at ambient temperatures, and there is little similarity. The paper will discuss the likely impact of CryoFD on future cryogenic systems, and hopefully inspire further research to support and expand the use of existing findings, and to improve the economy of present-day systems even more effectively. Particular examples to be mentioned include the following. Doubling the cooling power of cryo-coolers by a simple use of CryoFD. Reducing the boil-off rate of liquid helium stored at the South Pole, such that liquid helium availability is now all-the-year-round. Helping to develop the 15 kA current leads for the LHC superconducting magnets at CERN, with much reduced refrigeration loads. Improving the heat transfer capability of boiling heat transfer surfaces by 10 to 100 fold. This paper is an edited text of an invited plenary presentation at ICEC25/ICMC2014 by Professor Scurlock on the occasion of his being presented with the ICEC Mendelssohn Award for his many contributions to Cryogenics. As long ago as 1992, he first proposed in his "History and Origins of Cryogenics" that the temperature range for Cryogenics should be extended up to the ice-point at 273K. This paper expands on this proposal with the implicit assumption that Cryogenic Fluid Dynamics can provide a universal basis for modelling heat transfer and convective fluid behaviour of all fluids, at all temperatures, below the ice-point at 273K; or below 250K if you wish to exclude refrigeration engineering."

Deposition, accumulation, and alteration of Cl−, NO3−, ClO4− and ClO3− salts in a hyper-arid polar environment: Mass balance and isotopic constraints

USGS Publications Warehouse

Jackson, Andrew; Davila, Alfonso F.; Böhlke, John Karl; Sturchio, Neil C.; Sevanthi, Ritesh; Estrada, Nubia; Brundrett, Maeghan; Lacelle, Denis; McKay, Christopher P.; Poghosyan, Armen; Pollard, Wayne; Zacny, Kris

2016-01-01

The salt fraction in permafrost soils/sediments of the McMurdo Dry Valleys (MDV) of Antarctica can be used as a proxy for cold desert geochemical processes and paleoclimate reconstruction. Previous analyses of the salt fraction in MDV permafrost soils have largely been conducted in coastal regions where permafrost soils are variably affected by aqueous processes and mixed inputs from marine and stratospheric sources. We expand upon this work by evaluating permafrost soil/sediments in University Valley, located in the ultraxerous zone where both liquid water transport and marine influences are minimal. We determined the abundances of Cl−, NO3−, ClO4− and ClO3− in dry and ice-cemented soil/sediments, snow and glacier ice, and also characterized Cl− and NO3−isotopically. The data are not consistent with salt deposition in a sublimation till, nor with nuclear weapon testing fall-out, and instead point to a dominantly stratospheric source and to varying degrees of post depositional transformation depending on the substrate, from minimal alteration in bare soils to significant alteration (photodegradation and/or volatilization) in snow and glacier ice. Ionic abundances in the dry permafrost layer indicate limited vertical transport under the current climate conditions, likely due to percolation of snowmelt. Subtle changes in ClO4−/NO3− ratios and NO3− isotopic composition with depth and location may reflect both transport related fractionation and depositional history. Low molar ratios of ClO3−/ClO4− in surface soils compared to deposition and other arid systems suggest significant post depositional loss of ClO3−, possibly due to reduction by iron minerals, which may have important implications for oxy-chlorine species on Mars. Salt accumulation varies with distance along the valley and apparent accumulation times based on multiple methods range from ∼10 to 30 kyr near the glacier to 70–200 kyr near the valley mouth. The relatively young age of the salts and relatively low and homogeneous anion concentrations in the ice-cemented sediments point to either a mechanism of recent salt removal, or to relatively modern permafrost soils (<1 million years). Together, our results show that near surface salts in University Valley serve as an end-member of stratospheric sources not subject to biological processes or extensive remobilization.

The triggering factors of the Móafellshyrna debris slide in northern Iceland: intense precipitation, earthquake activity and thawing of mountain permafrost

NASA Astrophysics Data System (ADS)

Saemundsson, Thorsteinn; Morino, Costanza; Kristinn Helgason, Jón; Conway, Susan J.; Pétursson, Halldór G.

2017-04-01

On the 20th of September in 2012, a large debris slide occurred in the Móafellshyrna Mountain in the Tröllaskagi peninsula, central north Iceland. Three factors are likely to have contributed to the failure of the slope: intense precipitation, earthquake activity and thawing of ground ice. The weather conditions prior the slide were somewhat unusual, with a warm and dry summer. From the 20th of August to the 20th of September, about 440 mm of precipitation fell in the area, where the mean annual precipitation at the nearest station is around 670 mm. The slide initiated after this thirty day period of intense precipitation, followed by a seismic sequence in the Eyjafjarðaráll graben, located about 60 km NNE of Móafellshyrna Mountain, a sequence that started on the 19th of September. The slide originated at elevation of 870 m a.s.l. on the NW-slope of the mountain. The total volume of the debris slide is estimated around 500,000 m3 and that its primary cause was intense precipitation. We cannot exclude the influence of the seismic sequence as a secondary contributing factor. The presence of ice-cemented blocks of talus immediately after the debris slide shows that thawing of ground ice could also have played an important role as a triggering factor. Ice-cemented blocks of talus have been observed in the deposits of two other recent landslides in northern Iceland, in the Torfufell Mountain and the Árnesfjall Mountain. The source areas for both the Móafellshyrna and the Torfufell slides are within the lower elevation limit of mountain permafrost in northern Iceland but the source area of the Árnesfjall slide is at much lower elevation, around 350 m a.s.l. The fact that there are now three documented landslides which are linked to ground ice-melting suggests that discontinuous permafrost is degrading in Iceland, consistent with the decadal trend of increasing atmospheric temperature in Iceland due to climate change. This study highlights that ground ice thaw could represent a new source of hazard in Iceland. The knowledge of the detailed distribution of mountain permafrost on the island is poorly constrained, making it is hard to predict where the next hazardous slide could occur in the future - therefore a making this a priority for future research.

Hybrid evolutionary computing model for mobile agents of wireless Internet multimedia

NASA Astrophysics Data System (ADS)

Hortos, William S.

2001-03-01

The ecosystem is used as an evolutionary paradigm of natural laws for the distributed information retrieval via mobile agents to allow the computational load to be added to server nodes of wireless networks, while reducing the traffic on communication links. Based on the Food Web model, a set of computational rules of natural balance form the outer stage to control the evolution of mobile agents providing multimedia services with a wireless Internet protocol WIP. The evolutionary model shows how mobile agents should behave with the WIP, in particular, how mobile agents can cooperate, compete and learn from each other, based on an underlying competition for radio network resources to establish the wireless connections to support the quality of service QoS of user requests. Mobile agents are also allowed to clone themselves, propagate and communicate with other agents. A two-layer model is proposed for agent evolution: the outer layer is based on the law of natural balancing, the inner layer is based on a discrete version of a Kohonen self-organizing feature map SOFM to distribute network resources to meet QoS requirements. The former is embedded in the higher OSI layers of the WIP, while the latter is used in the resource management procedures of Layer 2 and 3 of the protocol. Algorithms for the distributed computation of mobile agent evolutionary behavior are developed by adding a learning state to the agent evolution state diagram. When an agent is in an indeterminate state, it can communicate to other agents. Computing models can be replicated from other agents. Then the agents transitions to the mutating state to wait for a new information-retrieval goal. When a wireless terminal or station lacks a network resource, an agent in the suspending state can change its policy to submit to the environment before it transitions to the searching state. The agents learn the facts of agent state information entered into an external database. In the cloning process, two agents on a host station sharing a common goal can be merged or married to compose a new agent. Application of the two-layer set of algorithms for mobile agent evolution, performed in a distributed processing environment, is made to the QoS management functions of the IP multimedia IM sub-network of the third generation 3G Wideband Code-division Multiple Access W-CDMA wireless network.

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

PubMed Central

Cornuet, Jean-Marie; Santos, Filipe; Beaumont, Mark A.; Robert, Christian P.; Marin, Jean-Michel; Balding, David J.; Guillemaud, Thomas; Estoup, Arnaud

2008-01-01

Summary: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. Availability: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc. Contact: j.cornuet@imperial.ac.uk Supplementary information: Supplementary data are also available at http://www.montpellier.inra.fr/CBGP/diyabc PMID:18842597

Evolutionary Study of Interethnic Cooperation

NASA Astrophysics Data System (ADS)

Kvasnicka, Vladimir; Pospichal, Jiri

The purpose of this communication is to present an evolutionary study of cooperation between two ethnic groups. The used model is stimulated by the seminal paper of J. D. Fearon and D. D. Laitin (Explaining Interethnic Cooperation, American Political Science Review, 90 (1996), pp. 715-735), where the iterated prisoner's dilemma was used to model intra- and interethnic interactions. We reformulated their approach in a form of evolutionary prisoner's dilemma method, where a population of strategies is evolved by applying simple reproduction process with a Darwin metaphor of natural selection (a probability of selection to the reproduction is proportional to a fitness). Our computer simulations show that an application of a principle of collective guilt does not lead to an emergence of an interethnic cooperation. When an administrator is introduced, then an emergence of interethnic cooperation may be observed. Furthermore, if the ethnic groups are of very different sizes, then the principle of collective guilt may be very devastating for smaller group so that intraethnic cooperation is destroyed. The second strategy of cooperation is called the personal responsibility, where agents that defected within interethnic interactions are punished inside of their ethnic groups. It means, unlikely to the principle of collective guilt, that there exists only one type of punishment, loosely speaking, agents are punished "personally." All the substantial computational results were checked and interpreted analytically within the theory of evolutionary stable strategies. Moreover, this theoretical approach offers mechanisms of simple scenarios explaining why some particular strategies are stable or not.

Pareto-optimal phylogenetic tree reconciliation

PubMed Central

Libeskind-Hadas, Ran; Wu, Yi-Chieh; Bansal, Mukul S.; Kellis, Manolis

2014-01-01

Motivation: Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem. Results: We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies. Availability and implementation: Our Python tools are freely available at www.cs.hmc.edu/∼hadas/xscape. Contact: mukul@engr.uconn.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24932009

Computational Evolutionary Methodology for Knowledge Discovery and Forecasting in Epidemiology and Medicine

NASA Astrophysics Data System (ADS)

Rao, Dhananjai M.; Chernyakhovsky, Alexander; Rao, Victoria

2008-05-01

Humanity is facing an increasing number of highly virulent and communicable diseases such as avian influenza. Researchers believe that avian influenza has potential to evolve into one of the deadliest pandemics. Combating these diseases requires in-depth knowledge of their epidemiology. An effective methodology for discovering epidemiological knowledge is to utilize a descriptive, evolutionary, ecological model and use bio-simulations to study and analyze it. These types of bio-simulations fall under the category of computational evolutionary methods because the individual entities participating in the simulation are permitted to evolve in a natural manner by reacting to changes in the simulated ecosystem. This work describes the application of the aforementioned methodology to discover epidemiological knowledge about avian influenza using a novel eco-modeling and bio-simulation environment called SEARUMS. The mathematical principles underlying SEARUMS, its design, and the procedure for using SEARUMS are discussed. The bio-simulations and multi-faceted case studies conducted using SEARUMS elucidate its ability to pinpoint timelines, epicenters, and socio-economic impacts of avian influenza. This knowledge is invaluable for proactive deployment of countermeasures in order to minimize negative socioeconomic impacts, combat the disease, and avert a pandemic.

Computational Evolutionary Methodology for Knowledge Discovery and Forecasting in Epidemiology and Medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, Dhananjai M.; Chernyakhovsky, Alexander; Rao, Victoria

2008-05-08

Humanity is facing an increasing number of highly virulent and communicable diseases such as avian influenza. Researchers believe that avian influenza has potential to evolve into one of the deadliest pandemics. Combating these diseases requires in-depth knowledge of their epidemiology. An effective methodology for discovering epidemiological knowledge is to utilize a descriptive, evolutionary, ecological model and use bio-simulations to study and analyze it. These types of bio-simulations fall under the category of computational evolutionary methods because the individual entities participating in the simulation are permitted to evolve in a natural manner by reacting to changes in the simulated ecosystem. Thismore » work describes the application of the aforementioned methodology to discover epidemiological knowledge about avian influenza using a novel eco-modeling and bio-simulation environment called SEARUMS. The mathematical principles underlying SEARUMS, its design, and the procedure for using SEARUMS are discussed. The bio-simulations and multi-faceted case studies conducted using SEARUMS elucidate its ability to pinpoint timelines, epicenters, and socio-economic impacts of avian influenza. This knowledge is invaluable for proactive deployment of countermeasures in order to minimize negative socioeconomic impacts, combat the disease, and avert a pandemic.« less

The tangled bank of amino acids.

PubMed

Goldstein, Richard A; Pollock, David D

2016-07-01

The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Evolving binary classifiers through parallel computation of multiple fitness cases.

PubMed

Cagnoni, Stefano; Bergenti, Federico; Mordonini, Monica; Adorni, Giovanni

2005-06-01

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

Computational Intelligence and Its Impact on Future High-Performance Engineering Systems

NASA Technical Reports Server (NTRS)

Noor, Ahmed K. (Compiler)

1996-01-01

This document contains presentations from the joint UVA/NASA Workshop on Computational Intelligence held at the Virginia Consortium of Engineering and Science Universities, Hampton, Virginia, June 27-28, 1995. The presentations addressed activities in the areas of fuzzy logic, neural networks, and evolutionary computations. Workshop attendees represented NASA, the National Science Foundation, the Department of Energy, National Institute of Standards and Technology (NIST), the Jet Propulsion Laboratory, industry, and academia. The workshop objectives were to assess the state of technology in the Computational intelligence area and to provide guidelines for future research.

Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

DTIC Science & Technology

2005-06-16

which is introduced in 1990 by Richard Dawkins in his book ”The Selfish Gene .” [34] 356 E.5.7 Pareto Envelop-based Selection Algorithm I and II...IGC Intelligent Gene Collector . . . . . . . . . . . . . . . . . 59 OED Orthogonal Experimental Design . . . . . . . . . . . . . 59 MED Main Effect...complete one experiment 74 `′ The string length hold within the computer (can be longer than number of genes

The Application of Multiobjective Evolutionary Algorithms to an Educational Computational Model of Science Information Processing: A Computational Experiment in Science Education

ERIC Educational Resources Information Center

Lamb, Richard L.; Firestone, Jonah B.

2017-01-01

Conflicting explanations and unrelated information in science classrooms increase cognitive load and decrease efficiency in learning. This reduced efficiency ultimately limits one's ability to solve reasoning problems in the science. In reasoning, it is the ability of students to sift through and identify critical pieces of information that is of…

Growth Control and Disease Mechanisms in Computational Embryogeny

NASA Technical Reports Server (NTRS)

Shapiro, Andrew A.; Yogev, Or; Antonsson, Erik K.

2008-01-01

This paper presents novel approach to applying growth control and diseases mechanisms in computational embryogeny. Our method, which mimics fundamental processes from biology, enables individuals to reach maturity in a controlled process through a stochastic environment. Three different mechanisms were implemented; disease mechanisms, gene suppression, and thermodynamic balancing. This approach was integrated as part of a structural evolutionary model. The model evolved continuum 3-D structures which support an external load. By using these mechanisms we were able to evolve individuals that reached a fixed size limit through the growth process. The growth process was an integral part of the complete development process. The size of the individuals was determined purely by the evolutionary process where different individuals matured to different sizes. Individuals which evolved with these characteristics have been found to be very robust for supporting a wide range of external loads.

Can An Evolutionary Process Create English Text?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, David H.

Critics of the conventional theory of biological evolution have asserted that while natural processes might result in some limited diversity, nothing fundamentally new can arise from 'random' evolution. In response, biologists such as Richard Dawkins have demonstrated that a computer program can generate a specific short phrase via evolution-like iterations starting with random gibberish. While such demonstrations are intriguing, they are flawed in that they have a fixed, pre-specified future target, whereas in real biological evolution there is no fixed future target, but only a complicated 'fitness landscape'. In this study, a significantly more sophisticated evolutionary scheme is employed tomore » produce text segments reminiscent of a Charles Dickens novel. The aggregate size of these segments is larger than the computer program and the input Dickens text, even when comparing compressed data (as a measure of information content).« less

Evolution and Vaccination of Influenza Virus.

PubMed

Lam, Ham Ching; Bi, Xuan; Sreevatsan, Srinand; Boley, Daniel

2017-08-01

In this study, we present an application paradigm in which an unsupervised machine learning approach is applied to the high-dimensional influenza genetic sequences to investigate whether vaccine is a driving force to the evolution of influenza virus. We first used a visualization approach to visualize the evolutionary paths of vaccine-controlled and non-vaccine-controlled influenza viruses in a low-dimensional space. We then quantified the evolutionary differences between their evolutionary trajectories through the use of within- and between-scatter matrices computation to provide the statistical confidence to support the visualization results. We used the influenza surface Hemagglutinin (HA) gene for this study as the HA gene is the major target of the immune system. The visualization is achieved without using any clustering methods or prior information about the influenza sequences. Our results clearly showed that the evolutionary trajectories between vaccine-controlled and non-vaccine-controlled influenza viruses are different and vaccine as an evolution driving force cannot be completely eliminated.

The evolutionary dynamics of language.

PubMed

Steels, Luc; Szathmáry, Eörs

2018-02-01

The well-established framework of evolutionary dynamics can be applied to the fascinating open problems how human brains are able to acquire and adapt language and how languages change in a population. Schemas for handling grammatical constructions are the replicating unit. They emerge and multiply with variation in the brains of individuals and undergo selection based on their contribution to needed expressive power, communicative success and the reduction of cognitive effort. Adopting this perspective has two major benefits. (i) It makes a bridge to neurobiological models of the brain that have also adopted an evolutionary dynamics point of view, thus opening a new horizon for studying how human brains achieve the remarkably complex competence for language. And (ii) it suggests a new foundation for studying cultural language change as an evolutionary dynamics process. The paper sketches this novel perspective, provides references to empirical data and computational experiments, and points to open problems. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Theoretical Foundation of the RelTime Method for Estimating Divergence Times from Variable Evolutionary Rates

PubMed Central

Tamura, Koichiro; Tao, Qiqing; Kumar, Sudhir

2018-01-01

Abstract RelTime estimates divergence times by relaxing the assumption of a strict molecular clock in a phylogeny. It shows excellent performance in estimating divergence times for both simulated and empirical molecular sequence data sets in which evolutionary rates varied extensively throughout the tree. RelTime is computationally efficient and scales well with increasing size of data sets. Until now, however, RelTime has not had a formal mathematical foundation. Here, we show that the basis of the RelTime approach is a relative rate framework (RRF) that combines comparisons of evolutionary rates in sister lineages with the principle of minimum rate change between evolutionary lineages and their respective descendants. We present analytical solutions for estimating relative lineage rates and divergence times under RRF. We also discuss the relationship of RRF with other approaches, including the Bayesian framework. We conclude that RelTime will be useful for phylogenies with branch lengths derived not only from molecular data, but also morphological and biochemical traits. PMID:29893954

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

PubMed

Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

2016-01-01

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Evolutionary Construction of Block-Based Neural Networks in Consideration of Failure

NASA Astrophysics Data System (ADS)

Takamori, Masahito; Koakutsu, Seiichi; Hamagami, Tomoki; Hirata, Hironori

In this paper we propose a modified gene coding and an evolutionary construction in consideration of failure in evolutionary construction of Block-Based Neural Networks. In the modified gene coding, we arrange the genes of weights on a chromosome in consideration of the position relation of the genes of weight and structure. By the modified gene coding, the efficiency of search by crossover is increased. Thereby, it is thought that improvement of the convergence rate of construction and shortening of construction time can be performed. In the evolutionary construction in consideration of failure, the structure which is adapted for failure is built in the state where failure occured. Thereby, it is thought that BBNN can be reconstructed in a short time at the time of failure. To evaluate the proposed method, we apply it to pattern classification and autonomous mobile robot control problems. The computational experiments indicate that the proposed method can improve convergence rate of construction and shorten of construction and reconstruction time.

XTALOPT: An open-source evolutionary algorithm for crystal structure prediction

NASA Astrophysics Data System (ADS)

Lonie, David C.; Zurek, Eva

2011-02-01

The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely available to the scientific community for use and collaboration under the GNU Public License. Running time: User dependent. The program runs until stopped by the user.

Evolution, learning, and cognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Y.C.

1988-01-01

The book comprises more than fifteen articles in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.

Application of high technology in highway transportation.

DOT National Transportation Integrated Search

1985-01-01

Highway and traffic engineering practice is rapidly changing as communications technology and computer systems are being adopted to facilitate the work of the practitioners and expand their capabilities. This field has been an evolutionary one since ...

Numerical Control/Computer Aided Manufacturing (NC/CAM), A Descom Study

DTIC Science & Technology

1979-07-01

CAM machines operate directly from computers, but most get instructions in the form of punched tape. The applications of NC/CAM are virtually...Although most NC/CAM equipment is metal working, its applications include electronics manufacturing, glass making, food processing, materiel handling...drafting, woodworking, plastics and inspection, just to name a few. Numerical control, like most technologies, is an advancing and evolutionary process

Supermultiplicative Speedups of Probabilistic Model-Building Genetic Algorithms

DTIC Science & Technology

2009-02-01

physicists as well as practitioners in evolutionary computation. The project was later extended to the one-dimensional SK spin glass with power -law... Brasil ) 10. Yuji Sato (Hosei University, Japan) 11. Shunsukc Saruwatari (Tokyo University, Japan) 12. Jian-Hung Chen (Feng Chia University, Taiwan...scalability. In A. Tiwari, J. Knowlcs, E. Avincri, K. Dahal, and R. Roy (Eds.) Applications of Soft Computing: Recent Trends. Berlin: Springer (2006

Building v/s Exploring Models: Comparing Learning of Evolutionary Processes through Agent-based Modeling

NASA Astrophysics Data System (ADS)

Wagh, Aditi

Two strands of work motivate the three studies in this dissertation. Evolutionary change can be viewed as a computational complex system in which a small set of rules operating at the individual level result in different population level outcomes under different conditions. Extensive research has documented students' difficulties with learning about evolutionary change (Rosengren et al., 2012), particularly in terms of levels slippage (Wilensky & Resnick, 1999). Second, though building and using computational models is becoming increasingly common in K-12 science education, we know little about how these two modalities compare. This dissertation adopts agent-based modeling as a representational system to compare these modalities in the conceptual context of micro-evolutionary processes. Drawing on interviews, Study 1 examines middle-school students' productive ways of reasoning about micro-evolutionary processes to find that the specific framing of traits plays a key role in whether slippage explanations are cued. Study 2, which was conducted in 2 schools with about 150 students, forms the crux of the dissertation. It compares learning processes and outcomes when students build their own models or explore a pre-built model. Analysis of Camtasia videos of student pairs reveals that builders' and explorers' ways of accessing rules, and sense-making of observed trends are of a different character. Builders notice rules through available blocks-based primitives, often bypassing their enactment while explorers attend to rules primarily through the enactment. Moreover, builders' sense-making of observed trends is more rule-driven while explorers' is more enactment-driven. Pre and posttests reveal that builders manifest a greater facility with accessing rules, providing explanations manifesting targeted assembly. Explorers use rules to construct explanations manifesting non-targeted assembly. Interviews reveal varying degrees of shifts away from slippage in both modalities, with students who built models not incorporating slippage explanations in responses. Study 3 compares these modalities with a control using traditional activities. Pre and posttests reveal that the two modalities manifested greater facility with accessing and assembling rules than the control. The dissertation offers implications for the design of learning environments for evolutionary change, design of the two modalities based on their strengths and weaknesses, and teacher training for the same.

More efficient evolutionary strategies for model calibration with watershed model for demonstration

NASA Astrophysics Data System (ADS)

Baggett, J. S.; Skahill, B. E.

2008-12-01

Evolutionary strategies allow automatic calibration of more complex models than traditional gradient based approaches, but they are more computationally intensive. We present several efficiency enhancements for evolution strategies, many of which are not new, but when combined have been shown to dramatically decrease the number of model runs required for calibration of synthetic problems. To reduce the number of expensive model runs we employ a surrogate objective function for an adaptively determined fraction of the population at each generation (Kern et al., 2006). We demonstrate improvements to the adaptive ranking strategy that increase its efficiency while sacrificing little reliability and further reduce the number of model runs required in densely sampled parts of parameter space. Furthermore, we include a gradient individual in each generation that is usually not selected when the search is in a global phase or when the derivatives are poorly approximated, but when selected near a smooth local minimum can dramatically increase convergence speed (Tahk et al., 2007). Finally, the selection of the gradient individual is used to adapt the size of the population near local minima. We show, by incorporating these enhancements into the Covariance Matrix Adaption Evolution Strategy (CMAES; Hansen, 2006), that their synergetic effect is greater than their individual parts. This hybrid evolutionary strategy exploits smooth structure when it is present but degrades to an ordinary evolutionary strategy, at worst, if smoothness is not present. Calibration of 2D-3D synthetic models with the modified CMAES requires approximately 10%-25% of the model runs of ordinary CMAES. Preliminary demonstration of this hybrid strategy will be shown for watershed model calibration problems. Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. Larrañga, I. Inza and E. Bengoetxea (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. pp. 75-102, Springer Kern, S., N. Hansen and P. Koumoutsakos (2006). Local Meta-Models for Optimization Using Evolution Strategies. In Ninth International Conference on Parallel Problem Solving from Nature PPSN IX, Proceedings, pp.939-948, Berlin: Springer. Tahk, M., Woo, H., and Park. M, (2007). A hybrid optimization of evolutionary and gradient search. Engineering Optimization, (39), 87-104.

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

Evolutionary neurobiology and aesthetics.

PubMed

Smith, Christopher Upham

2005-01-01

If aesthetics is a human universal, it should have a neurobiological basis. Although use of all the senses is, as Aristotle noted, pleasurable, the distance senses are primarily involved in aesthetics. The aesthetic response emerges from the central processing of sensory input. This occurs very rapidly, beneath the level of consciousness, and only the feeling of pleasure emerges into the conscious mind. This is exemplified by landscape appreciation, where it is suggested that a computation built into the nervous system during Paleolithic hunter-gathering is at work. Another inbuilt computation leading to an aesthetic response is the part-whole relationship. This, it is argued, may be traced to the predator-prey "arms races" of evolutionary history. Mate selection also may be responsible for part of our response to landscape and visual art. Aesthetics lies at the core of human mentality, and its study is consequently of importance not only to philosophers and art critics but also to neurobiologists.

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

NASA Astrophysics Data System (ADS)

Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

2015-08-01

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Decentralized Grid Scheduling with Evolutionary Fuzzy Systems

NASA Astrophysics Data System (ADS)

Fölling, Alexander; Grimme, Christian; Lepping, Joachim; Papaspyrou, Alexander

In this paper, we address the problem of finding workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a non-invasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.

Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.

Evolutionary psychology: new perspectives on cognition and motivation.

PubMed

Cosmides, Leda; Tooby, John

2013-01-01

Evolutionary psychology is the second wave of the cognitive revolution. The first wave focused on computational processes that generate knowledge about the world: perception, attention, categorization, reasoning, learning, and memory. The second wave views the brain as composed of evolved computational systems, engineered by natural selection to use information to adaptively regulate physiology and behavior. This shift in focus--from knowledge acquisition to the adaptive regulation of behavior--provides new ways of thinking about every topic in psychology. It suggests a mind populated by a large number of adaptive specializations, each equipped with content-rich representations, concepts, inference systems, and regulatory variables, which are functionally organized to solve the complex problems of survival and reproduction encountered by the ancestral hunter-gatherers from whom we are descended. We present recent empirical examples that illustrate how this approach has been used to discover new features of attention, categorization, reasoning, learning, emotion, and motivation.

Recombinant transfer in the basic genome of E. coli

DOE PAGES

Dixit, Purushottam; Studier, F. William; Pang, Tin Yau; ...

2015-07-07

An approximation to the ~4-Mbp basic genome shared by 32 strains of E. coli representing six evolutionary groups has been derived and analyzed computationally. A multiple-alignment of the 32 complete genome sequences was filtered to remove mobile elements and identify the most reliable ~90% of the aligned length of each of the resulting 496 basic-genome pairs. Patterns of single bp mutations (SNPs) in aligned pairs distinguish clonally inherited regions from regions where either genome has acquired DNA fragments from diverged genomes by homologous recombination since their last common ancestor. Such recombinant transfer is pervasive across the basic genome, mostly betweenmore » genomes in the same evolutionary group, and generates many unique mosaic patterns. The six least-diverged genome-pairs have one or two recombinant transfers of length ~40–115 kbp (and few if any other transfers), each containing one or more gene clusters known to confer strong selective advantage in some environments. Moderately diverged genome pairs (0.4–1% SNPs) show mosaic patterns of interspersed clonal and recombinant regions of varying lengths throughout the basic genome, whereas more highly diverged pairs within an evolutionary group or pairs between evolutionary groups having >1.3% SNPs have few clonal matches longer than a few kbp. Many recombinant transfers appear to incorporate fragments of the entering DNA produced by restriction systems of the recipient cell. A simple computational model can closely fit the data. As a result, most recombinant transfers seem likely to be due to generalized transduction by co-evolving populations of phages, which could efficiently distribute variability throughout bacterial genomes.« less

Recombinant transfer in the basic genome of E. coli

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dixit, Purushottam; Studier, F. William; Pang, Tin Yau

An approximation to the ~4-Mbp basic genome shared by 32 strains of E. coli representing six evolutionary groups has been derived and analyzed computationally. A multiple-alignment of the 32 complete genome sequences was filtered to remove mobile elements and identify the most reliable ~90% of the aligned length of each of the resulting 496 basic-genome pairs. Patterns of single bp mutations (SNPs) in aligned pairs distinguish clonally inherited regions from regions where either genome has acquired DNA fragments from diverged genomes by homologous recombination since their last common ancestor. Such recombinant transfer is pervasive across the basic genome, mostly betweenmore » genomes in the same evolutionary group, and generates many unique mosaic patterns. The six least-diverged genome-pairs have one or two recombinant transfers of length ~40–115 kbp (and few if any other transfers), each containing one or more gene clusters known to confer strong selective advantage in some environments. Moderately diverged genome pairs (0.4–1% SNPs) show mosaic patterns of interspersed clonal and recombinant regions of varying lengths throughout the basic genome, whereas more highly diverged pairs within an evolutionary group or pairs between evolutionary groups having >1.3% SNPs have few clonal matches longer than a few kbp. Many recombinant transfers appear to incorporate fragments of the entering DNA produced by restriction systems of the recipient cell. A simple computational model can closely fit the data. As a result, most recombinant transfers seem likely to be due to generalized transduction by co-evolving populations of phages, which could efficiently distribute variability throughout bacterial genomes.« less

Evolutionary dynamics on graphs: Efficient method for weak selection

NASA Astrophysics Data System (ADS)

Fu, Feng; Wang, Long; Nowak, Martin A.; Hauert, Christoph

2009-04-01

Investigating the evolutionary dynamics of game theoretical interactions in populations where individuals are arranged on a graph can be challenging in terms of computation time. Here, we propose an efficient method to study any type of game on arbitrary graph structures for weak selection. In this limit, evolutionary game dynamics represents a first-order correction to neutral evolution. Spatial correlations can be empirically determined under neutral evolution and provide the basis for formulating the game dynamics as a discrete Markov process by incorporating a detailed description of the microscopic dynamics based on the neutral correlations. This framework is then applied to one of the most intriguing questions in evolutionary biology: the evolution of cooperation. We demonstrate that the degree heterogeneity of a graph impedes cooperation and that the success of tit for tat depends not only on the number of rounds but also on the degree of the graph. Moreover, considering the mutation-selection equilibrium shows that the symmetry of the stationary distribution of states under weak selection is skewed in favor of defectors for larger selection strengths. In particular, degree heterogeneity—a prominent feature of scale-free networks—generally results in a more pronounced increase in the critical benefit-to-cost ratio required for evolution to favor cooperation as compared to regular graphs. This conclusion is corroborated by an analysis of the effects of population structures on the fixation probabilities of strategies in general 2×2 games for different types of graphs. Computer simulations confirm the predictive power of our method and illustrate the improved accuracy as compared to previous studies.

Computational complexity of ecological and evolutionary spatial dynamics

PubMed Central

Ibsen-Jensen, Rasmus; Chatterjee, Krishnendu; Nowak, Martin A.

2015-01-01

There are deep, yet largely unexplored, connections between computer science and biology. Both disciplines examine how information proliferates in time and space. Central results in computer science describe the complexity of algorithms that solve certain classes of problems. An algorithm is deemed efficient if it can solve a problem in polynomial time, which means the running time of the algorithm is a polynomial function of the length of the input. There are classes of harder problems for which the fastest possible algorithm requires exponential time. Another criterion is the space requirement of the algorithm. There is a crucial distinction between algorithms that can find a solution, verify a solution, or list several distinct solutions in given time and space. The complexity hierarchy that is generated in this way is the foundation of theoretical computer science. Precise complexity results can be notoriously difficult. The famous question whether polynomial time equals nondeterministic polynomial time (i.e., P = NP) is one of the hardest open problems in computer science and all of mathematics. Here, we consider simple processes of ecological and evolutionary spatial dynamics. The basic question is: What is the probability that a new invader (or a new mutant) will take over a resident population? We derive precise complexity results for a variety of scenarios. We therefore show that some fundamental questions in this area cannot be answered by simple equations (assuming that P is not equal to NP). PMID:26644569

The Proposal of a Evolutionary Strategy Generating the Data Structures Based on a Horizontal Tree for the Tests

NASA Astrophysics Data System (ADS)

Żukowicz, Marek; Markiewicz, Michał

2016-09-01

The aim of the article is to present a mathematical definition of the object model, that is known in computer science as TreeList and to show application of this model for design evolutionary algorithm, that purpose is to generate structures based on this object. The first chapter introduces the reader to the problem of presenting data using the TreeList object. The second chapter describes the problem of testing data structures based on TreeList. The third one shows a mathematical model of the object TreeList and the parameters, used in determining the utility of structures created through this model and in evolutionary strategy, that generates these structures for testing purposes. The last chapter provides a brief summary and plans for future research related to the algorithm presented in the article.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation.

PubMed

Ingalls, Brian; Mincheva, Maya; Roussel, Marc R

2017-07-01

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

Detecting and Analyzing Genetic Recombination Using RDP4.

PubMed

Martin, Darren P; Murrell, Ben; Khoosal, Arjun; Muhire, Brejnev

2017-01-01

Recombination between nucleotide sequences is a major process influencing the evolution of most species on Earth. The evolutionary value of recombination has been widely debated and so too has its influence on evolutionary analysis methods that assume nucleotide sequences replicate without recombining. When nucleic acids recombine, the evolution of the daughter or recombinant molecule cannot be accurately described by a single phylogeny. This simple fact can seriously undermine the accuracy of any phylogenetics-based analytical approach which assumes that the evolutionary history of a set of recombining sequences can be adequately described by a single phylogenetic tree. There are presently a large number of available methods and associated computer programs for analyzing and characterizing recombination in various classes of nucleotide sequence datasets. Here we examine the use of some of these methods to derive and test recombination hypotheses using multiple sequence alignments.

Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

DOE PAGES

Jacobs, Christopher; Lambourne, Luke; Xia, Yu; ...

2017-01-20

Here, system-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now"º and the same gene's historical importance asmore » evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.« less

Protein 3D Structure Computed from Evolutionary Sequence Variation

PubMed Central

Sheridan, Robert; Hopf, Thomas A.; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2011-01-01

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes. PMID:22163331

Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs, Christopher; Lambourne, Luke; Xia, Yu

Here, system-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now"º and the same gene's historical importance asmore » evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.« less

Faster Evolution of More Multifunctional Logic Circuits

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Zebulum, Ricardo

2005-01-01

A modification in a method of automated evolutionary synthesis of voltage-controlled multifunctional logic circuits makes it possible to synthesize more circuits in less time. Prior to the modification, the computations for synthesizing a four-function logic circuit by this method took about 10 hours. Using the method as modified, it is possible to synthesize a six-function circuit in less than half an hour. The concepts of automated evolutionary synthesis and voltage-controlled multifunctional logic circuits were described in a number of prior NASA Tech Briefs articles. To recapitulate: A circuit is designed to perform one of several different logic functions, depending on the value of an applied control voltage. The circuit design is synthesized following an automated evolutionary approach that is so named because it is modeled partly after the repetitive trial-and-error process of biological evolution. In this process, random populations of integer strings that encode electronic circuits play a role analogous to that of chromosomes. An evolved circuit is tested by computational simulation (prior to testing in real hardware to verify a final design). Then, in a fitness-evaluation step, responses of the circuit are compared with specifications of target responses and circuits are ranked according to how close they come to satisfying specifications. The results of the evaluation provide guidance for refining designs through further iteration.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

PubMed

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

Nature-Inspired Cognitive Evolution to Play MS. Pac-Man

NASA Astrophysics Data System (ADS)

Tan, Tse Guan; Teo, Jason; Anthony, Patricia

Recent developments in nature-inspired computation have heightened the need for research into the three main areas of scientific, engineering and industrial applications. Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. So far however, there has been little discussion about the effectiveness of the application of these models to computer and video games in particular. The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. This paper investigates the abilities of Evolution Strategies (ES) to evolve feed-forward artificial neural network's internal parameters (i.e. weight and bias values) for automatically generating Ms. Pac-man controllers. The main objective of this game is to clear a maze of dots while avoiding the ghosts and to achieve the highest possible score. The experimental results show that an ES-based system can be successfully applied to automatically generate artificial intelligence for a complex, dynamic and highly stochastic video game environment.

Analysis of Water Extraction From Lunar Regolith

NASA Technical Reports Server (NTRS)

Hegde, U.; Balasubramaniam, R.; Gokoglu, S.

2012-01-01

Distribution of water concentration on the Moon is currently an area of active research. Recent studies suggest the presence of ice particles, and perhaps even ice blocks and ice-cemented regolith on the Moon. Thermal extraction of the in-situ water is an attractive means of sa tisfying water requirements for a lunar mission. In this paper, a model is presented to analyze the processes occurring during the heat-up of icy regolith and extraction of the evolved water vapor. The wet regolith is assumed to be present in an initially evacuated and sealed cell which is subsequently heated. The first step of the analysis invol ves calculating the gradual increase of vapor pressure in the closed cell as the temperature is raised. Then, in the second step, the cell is evacuated to low pressure (e.g., vacuum), allowing the water vapor to leave the cell and be captured. The parameters affecting water vap or pressure build-up and evacuation for the purpose of extracting water from lunar regolith are discussed in the paper. Some comparisons wi th available experimental measurements are also made.

Variations on a Theme.

ERIC Educational Resources Information Center

Vitali, Julius

1990-01-01

Explains an experimental photographic technique starting with a realistic photograph. Using various media (oil painting, video/computer photography, and multiprint imagery) the artist changes the photograph's compositional elements. Outlines the phases of this evolutionary process. Illustrates four images created by the technique. (DB)

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

2008-01-01

other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP 󈨦), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third

Numerical simulation of evolutionary erodible bedforms using the particle finite element method

NASA Astrophysics Data System (ADS)

Bravo, Rafael; Becker, Pablo; Ortiz, Pablo

2017-07-01

This paper presents a numerical strategy for the simulation of flows with evolutionary erodible boundaries. The fluid equations are fully resolved in 3D, while the sediment transport is modelled using the Exner equation and solved with an explicit Lagrangian procedure based on a fixed 2D mesh. Flow and sediment are coupled in geometry by deforming the fluid mesh in the vertical direction and in velocities with the experimental sediment flux computed using the Meyer Peter Müller model. A comparison with real experiments on channels is performed, giving good agreement.

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

NASA Astrophysics Data System (ADS)

Song, Chen; Zhong-Cheng, Wu; Hong, Lv

2018-03-01

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

Geometric morphometrics and virtual anthropology: advances in human evolutionary studies.

PubMed

Rein, Thomas R; Harvati, Katerina

2014-01-01

Geometric morphometric methods have been increasingly used in paleoanthropology in the last two decades, lending greater power to the analysis and interpretation of the human fossil record. More recently the advent of the wide use of computed tomography and surface scanning, implemented in combination with geometric morphometrics (GM), characterizes a new approach, termed Virtual Anthropology (VA). These methodological advances have led to a number of developments in human evolutionary studies. We present some recent examples of GM and VA related research in human evolution with an emphasis on work conducted at the University of Tübingen and other German research institutions.

VizieR Online Data Catalog: Low-mass helium white dwarfs evolutionary models (Istrate+, 2016)

NASA Astrophysics Data System (ADS)

Istrate, A.; Marchant, P.; Tauris, T. M.; Langer, N.; Stancliffe, R. J.; Grassitelli, L.

2016-07-01

Evolutionary models of low-mass helium white dwarfs including element diffusion and rotational mixing. The WDs are produced considering binary evolution through the LMXB channel, with final WDs masses between ~0.16-~0.44. The models are computed using MESA, for different metallicities: Z=0.02, 0.01, 0.001 and 0.0002. For each metallicity, the models are divided in three categories: (1) basic (no diffusion nor rotation are considered) (2) diffusion (element diffusion is considered) (3) rotation+diffusion (both element diffusion and rotational mixing are considered) (4 data files).

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

NASA Astrophysics Data System (ADS)

Pini, Giovanni; Tuci, Elio

2008-06-01

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Evolutionary Trails of Plant Group II Pyridoxal Phosphate-Dependent Decarboxylase Genes.

PubMed

Kumar, Rahul

2016-01-01

Type II pyridoxal phosphate-dependent decarboxylase (PLP_deC) enzymes play important metabolic roles during nitrogen metabolism. Recent evolutionary profiling of these genes revealed a sharp expansion of histidine decarboxylase genes in the members of Solanaceae family. In spite of the high sequence homology shared by PLP_deC orthologs, these enzymes display remarkable differences in their substrate specificities. Currently, limited information is available on the gene repertoires and substrate specificities of PLP_deCs which renders their precise annotation challenging and offers technical challenges in the immediate identification and biochemical characterization of their full gene complements in plants. Herein, we explored their evolutionary trails in a comprehensive manner by taking advantage of high-throughput data accessibility and computational approaches. We discussed the premise that has enabled an improved reconstruction of their evolutionary lineage and evaluated the factors offering constraints in their rapid functional characterization, till date. We envisage that the synthesized information herein would act as a catalyst for the rapid exploration of their biochemical specificity and physiological roles in more plant species.

Evolutionary design optimization of traffic signals applied to Quito city.

PubMed

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

Evolutionary design optimization of traffic signals applied to Quito city

PubMed Central

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process. PMID:29236733

Underlying Principles of Natural Selection in Network Evolution: Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Wu, Wei-Sheng

2007-01-01

Systems biology is a rapidly expanding field that integrates diverse areas of science such as physics, engineering, computer science, mathematics, and biology toward the goal of elucidating the underlying principles of hierarchical metabolic and regulatory systems in the cell, and ultimately leading to predictive understanding of cellular response to perturbations. Because post-genomics research is taking place throughout the tree of life, comparative approaches offer a way for combining data from many organisms to shed light on the evolution and function of biological networks from the gene to the organismal level. Therefore, systems biology can build on decades of theoretical work in evolutionary biology, and at the same time evolutionary biology can use the systems biology approach to go in new uncharted directions. In this study, we present a review of how the post-genomics era is adopting comparative approaches and dynamic system methods to understand the underlying design principles of network evolution and to shape the nascent field of evolutionary systems biology. Finally, the application of evolutionary systems biology to robust biological network designs is also discussed from the synthetic biology perspective. PMID:19468310

Evolutionary game theory using agent-based methods.

PubMed

Adami, Christoph; Schossau, Jory; Hintze, Arend

2016-12-01

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Evolution-Inspired Computational Design of Symmetric Proteins.

PubMed

Voet, Arnout R D; Simoncini, David; Tame, Jeremy R H; Zhang, Kam Y J

2017-01-01

Monomeric proteins with a number of identical repeats creating symmetrical structures are potentially very valuable building blocks with a variety of bionanotechnological applications. As such proteins do not occur naturally, the emerging field of computational protein design serves as an excellent tool to create them from nonsymmetrical templates. Existing pseudo-symmetrical proteins are believed to have evolved from oligomeric precursors by duplication and fusion of identical repeats. Here we describe a computational workflow to reverse-engineer this evolutionary process in order to create stable proteins consisting of identical sequence repeats.

Computers in health care for the 21st century.

PubMed

O'Desky, R I; Ball, M J; Ball, E E

1990-03-01

As the world enters the last decade of the 20th Century, there is a great deal of speculation about the effect of computers on the future delivery of health care. In this article, the authors attempt to identify some of the evolving computer technologies and anticipate what effect they will have by the year 2000. Rather than listing potential accomplishments, each of the affected areas: hardware, software, health care systems and communications, are presented in an evolutionary manner so the reader can better appreciate where we have been and where we are going.

Evolutionary and biological metaphors for engineering design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jakiela, M.

1994-12-31

Since computing became generally available, there has been strong interest in using computers to assist and automate engineering design processes. Specifically, for design optimization and automation, nonlinear programming and artificial intelligence techniques have been extensively studied. New computational techniques, based upon the natural processes of evolution, adaptation, and learing, are showing promise because of their generality and robustness. This presentation will describe the use of two such techniques, genetic algorithms and classifier systems, for a variety of engineering design problems. Structural topology optimization, meshing, and general engineering optimization are shown as example applications.

Launching "the evolution of cooperation".

PubMed

Axelrod, Robert

2012-04-21

This article describes three aspects of the author's early work on the evolution of the cooperation. First, it explains how the idea for a computer tournament for the iterated Prisoner's Dilemma was inspired by the artificial intelligence research on computer checkers and computer chess. Second, it shows how the vulnerability of simple reciprocity of misunderstanding or misimplementation can be eliminated with the addition of some degree of generosity or contrition. Third, it recounts the unusual collaboration between the author, a political scientist, and William D. Hamilton, an evolutionary biologist. Copyright © 2011 Elsevier Ltd. All rights reserved.

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

PubMed Central

Vrancken, Bram; Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Longdon, Ben; Günthard, Huldrych F.; Suchard, Marc A.

2014-01-01

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel’s λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution. PMID:25780554

Multidisciplinary Approaches in Evolutionary Linguistics

ERIC Educational Resources Information Center

Gong, Tao; Shuai, Lan; Wu, Yicheng

2013-01-01

Studying language evolution has become resurgent in modern scientific research. In this revival field, approaches from a number of disciplines other than linguistics, including (paleo)anthropology and archaeology, animal behaviors, genetics, neuroscience, computer simulation, and psychological experimentation, have been adopted, and a wide scope…

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

Evolutionary dynamics on any population structure

NASA Astrophysics Data System (ADS)

Allen, Benjamin; Lippner, Gabor; Chen, Yu-Ting; Fotouhi, Babak; Momeni, Naghmeh; Yau, Shing-Tung; Nowak, Martin A.

2017-03-01

Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure—graph surgery—affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

Turbopump Performance Improved by Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2002-01-01

The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.

Evolution of cyclohexadienyl dehydratase from an ancestral solute-binding protein.

PubMed

Clifton, Ben E; Kaczmarski, Joe A; Carr, Paul D; Gerth, Monica L; Tokuriki, Nobuhiko; Jackson, Colin J

2018-04-23

The emergence of enzymes through the neofunctionalization of noncatalytic proteins is ultimately responsible for the extraordinary range of biological catalysts observed in nature. Although the evolution of some enzymes from binding proteins can be inferred by homology, we have a limited understanding of the nature of the biochemical and biophysical adaptations along these evolutionary trajectories and the sequence in which they occurred. Here we reconstructed and characterized evolutionary intermediate states linking an ancestral solute-binding protein to the extant enzyme cyclohexadienyl dehydratase. We show how the intrinsic reactivity of a desolvated general acid was harnessed by a series of mutations radiating from the active site, which optimized enzyme-substrate complementarity and transition-state stabilization and minimized sampling of noncatalytic conformations. Our work reveals the molecular evolutionary processes that underlie the emergence of enzymes de novo, which are notably mirrored by recent examples of computational enzyme design and directed evolution.

Towards resolving the complete fern tree of life.

PubMed

Lehtonen, Samuli

2011-01-01

In the past two decades, molecular systematic studies have revolutionized our understanding of the evolutionary history of ferns. The availability of large molecular data sets together with efficient computer algorithms, now enables us to reconstruct evolutionary histories with previously unseen completeness. Here, the most comprehensive fern phylogeny to date, representing over one-fifth of the extant global fern diversity, is inferred based on four plastid genes. Parsimony and maximum-likelihood analyses provided a mostly congruent results and in general supported the prevailing view on the higher-level fern systematics. At a deep phylogenetic level, the position of horsetails depended on the optimality criteria chosen, with horsetails positioned as the sister group either of Marattiopsida-Polypodiopsida clade or of the Polypodiopsida. The analyses demonstrate the power of using a 'supermatrix' approach to resolve large-scale phylogenies and reveal questionable taxonomies. These results provide a valuable background for future research on fern systematics, ecology, biogeography and other evolutionary studies.

The Path of the Blind Watchmaker: A Model of Evolution

DTIC Science & Technology

2011-04-06

computational biology has now reached the point that astronomy reached when it began to look backward in time to the Big Bang. Our goal is look backward in...treatment. We claim that computational biology has now reached the point that astronomy reached when it began to look backward in time to the Big...evolutionary process itself, in fact, created it. When astronomy reached a critical mass of theory, technology, and observational data, astronomers

CSI computer system/remote interface unit acceptance test results

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.

1992-01-01

The validation tests conducted on the Control/Structures Interaction (CSI) Computer System (CCS)/Remote Interface Unit (RIU) is discussed. The CCS/RIU consists of a commercially available, Langley Research Center (LaRC) programmed, space flight qualified computer and a flight data acquisition and filtering computer, developed at LaRC. The tests were performed in the Space Structures Research Laboratory (SSRL) and included open loop excitation, closed loop control, safing, RIU digital filtering, and RIU stand alone testing with the CSI Evolutionary Model (CEM) Phase-0 testbed. The test results indicated that the CCS/RIU system is comparable to ground based systems in performing real-time control-structure experiments.

DeCoSTAR: Reconstructing the Ancestral Organization of Genes or Genomes Using Reconciled Phylogenies

PubMed Central

Anselmetti, Yoann; Patterson, Murray; Ponty, Yann; B�rard, S�verine; Chauve, Cedric; Scornavacca, Celine; Daubin, Vincent; Tannier, Eric

2017-01-01

DeCoSTAR is a software that aims at reconstructing the organization of ancestral genes or genomes in the form of sets of neighborhood relations (adjacencies) between pairs of ancestral genes or gene domains. It can also improve the assembly of fragmented genomes by proposing evolutionary-induced adjacencies between scaffolding fragments. Ancestral genes or domains are deduced from reconciled phylogenetic trees under an evolutionary model that considers gains, losses, speciations, duplications, and transfers as possible events for gene evolution. Reconciliations are either given as input or computed with the ecceTERA package, into which DeCoSTAR is integrated. DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on a weighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann–Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided. DeCoSTAR supports the features of previously contributed tools that reconstruct ancestral adjacencies, namely DeCo, DeCoLT, ART-DeCo, and DeClone. In a few minutes, DeCoSTAR can reconstruct the evolutionary history of domains inside genes, of gene fusion and fission events, or of gene order along chromosomes, for large data sets including dozens of whole genomes from all kingdoms of life. We illustrate the potential of DeCoSTAR with several applications: ancestral reconstruction of gene orders for Anopheles mosquito genomes, multidomain proteins in Drosophila, and gene fusion and fission detection in Actinobacteria. Availability: http://pbil.univ-lyon1.fr/software/DeCoSTAR (Last accessed April 24, 2017). PMID:28402423

Self-organized modularization in evolutionary algorithms.

PubMed

Dauscher, Peter; Uthmann, Thomas

2005-01-01

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

AN ANGIOTENSIN II TYPE 1 RECEPTOR ACTIVATION SWITCH PATCH REVEALED THROUGH EVOLUTIONARY TRACE ANALYSIS

PubMed Central

Bonde, Marie Mi; Yao, Rong; Ma, Jian-Nong; Madabushi, Srinivasan; Haunsø, Stig; Burstein, Ethan S.; Whistler, Jennifer L.; Sheikh, Søren P.; Lichtarge, Olivier; Hansen, Jakob Lerche

2010-01-01

Seven transmembrane (7TM) or G protein-coupled receptors constitute a large superfamily of cell surface receptors sharing a structural motif of seven transmembrane spanning alpha helices. Their activation mechanism most likely involves concerted movements of the transmembrane helices, but remains to be completely resolved. Evolutionary Trace (ET) analysis is a computational method, which identifies clusters of functionally important residues by integrating information on evolutionary important residue variations with receptor structure. Combined with known mutational data, ET predicted a patch of residues in the cytoplasmic parts of TM2, TM3, and TM6 to form an activation switch that is common to all family A 7TM receptors. We tested this hypothesis in the rat Angiotensin II (Ang II) type 1 (AT1) receptor. The receptor has important roles in the cardiovascular system, but has also frequently been applied as a model for 7TM receptor activation and signaling. Six mutations: F66A, L67R, L70R, L119R, D125A, and I245F were targeted to the putative switch and assayed for changes in activation state by their ligand binding, signaling, and trafficking properties. All but one receptor mutant (that was not expressed well) displayed phenotypes associated with changed activation state, such as increased agonist affinity or basal activity, promiscuous activation, or constitutive internalization highlighting the importance of testing different signaling pathways. We conclude that this evolutionary important patch mediates interactions important for maintaining the inactive state. More broadly, these observations in the AT1 receptor are consistent with computational predictions of a generic role for this patch in 7TM receptor activation. PMID:20227396

Fast and asymptotic computation of the fixation probability for Moran processes on graphs.

PubMed

Alcalde Cuesta, F; González Sequeiros, P; Lozano Rojo, Á

2015-03-01

Evolutionary dynamics has been classically studied for homogeneous populations, but now there is a growing interest in the non-homogeneous case. One of the most important models has been proposed in Lieberman et al. (2005), adapting to a weighted directed graph the process described in Moran (1958). The Markov chain associated with the graph can be modified by erasing all non-trivial loops in its state space, obtaining the so-called Embedded Markov chain (EMC). The fixation probability remains unchanged, but the expected time to absorption (fixation or extinction) is reduced. In this paper, we shall use this idea to compute asymptotically the average fixation probability for complete bipartite graphs K(n,m). To this end, we firstly review some recent results on evolutionary dynamics on graphs trying to clarify some points. We also revisit the 'Star Theorem' proved in Lieberman et al. (2005) for the star graphs K(1,m). Theoretically, EMC techniques allow fast computation of the fixation probability, but in practice this is not always true. Thus, in the last part of the paper, we compare this algorithm with the standard Monte Carlo method for some kind of complex networks. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

PubMed

García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

2006-05-01

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Evolutionary Games of Multiplayer Cooperation on Graphs

PubMed Central

Arranz, Jordi; Traulsen, Arne

2016-01-01

There has been much interest in studying evolutionary games in structured populations, often modeled as graphs. However, most analytical results so far have only been obtained for two-player or linear games, while the study of more complex multiplayer games has been usually tackled by computer simulations. Here we investigate evolutionary multiplayer games on graphs updated with a Moran death-Birth process. For cycles, we obtain an exact analytical condition for cooperation to be favored by natural selection, given in terms of the payoffs of the game and a set of structure coefficients. For regular graphs of degree three and larger, we estimate this condition using a combination of pair approximation and diffusion approximation. For a large class of cooperation games, our approximations suggest that graph-structured populations are stronger promoters of cooperation than populations lacking spatial structure. Computer simulations validate our analytical approximations for random regular graphs and cycles, but show systematic differences for graphs with many loops such as lattices. In particular, our simulation results show that these kinds of graphs can even lead to more stringent conditions for the evolution of cooperation than well-mixed populations. Overall, we provide evidence suggesting that the complexity arising from many-player interactions and spatial structure can be captured by pair approximation in the case of random graphs, but that it need to be handled with care for graphs with high clustering. PMID:27513946

Perspective: Evolutionary design of granular media and block copolymer patterns

NASA Astrophysics Data System (ADS)

Jaeger, Heinrich M.; de Pablo, Juan J.

2016-05-01

The creation of new materials "by design" is a process that starts from desired materials properties and proceeds to identify requirements for the constituent components. Such process is challenging because it inverts the typical modeling approach, which starts from given micro-level components to predict macro-level properties. We describe how to tackle this inverse problem using concepts from evolutionary computation. These concepts have widespread applicability and open up new opportunities for design as well as discovery. Here we apply them to design tasks involving two very different classes of soft materials, shape-optimized granular media and nanopatterned block copolymer thin films.

An Evolutionary Comparison of the Handicap Principle and Hybrid Equilibrium Theories of Signaling.

PubMed

Kane, Patrick; Zollman, Kevin J S

2015-01-01

The handicap principle has come under significant challenge both from empirical studies and from theoretical work. As a result, a number of alternative explanations for honest signaling have been proposed. This paper compares the evolutionary plausibility of one such alternative, the "hybrid equilibrium," to the handicap principle. We utilize computer simulations to compare these two theories as they are instantiated in Maynard Smith's Sir Philip Sidney game. We conclude that, when both types of communication are possible, evolution is unlikely to lead to handicap signaling and is far more likely to result in the partially honest signaling predicted by hybrid equilibrium theory.

Evolutionary Development of the Simulation by Logical Modeling System (SIBYL)

NASA Technical Reports Server (NTRS)

Wu, Helen

1995-01-01

Through the evolutionary development of the Simulation by Logical Modeling System (SIBYL) we have re-engineered the expensive and complex IBM mainframe based Long-term Hardware Projection Model (LHPM) to a robust cost-effective computer based mode that is easy to use. We achieved significant cost reductions and improved productivity in preparing long-term forecasts of Space Shuttle Main Engine (SSME) hardware. The LHPM for the SSME is a stochastic simulation model that projects the hardware requirements over 10 years. SIBYL is now the primary modeling tool for developing SSME logistics proposals and Program Operating Plan (POP) for NASA and divisional marketing studies.

HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system.

PubMed

Majid, Abdul; Ali, Safdar

2015-01-01

We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.

Capability of the Maximax&Maximin selection operator in the evolutionary algorithm for a nurse scheduling problem

NASA Astrophysics Data System (ADS)

Ramli, Razamin; Tein, Lim Huai

2016-08-01

A good work schedule can improve hospital operations by providing better coverage with appropriate staffing levels in managing nurse personnel. Hence, constructing the best nurse work schedule is the appropriate effort. In doing so, an improved selection operator in the Evolutionary Algorithm (EA) strategy for a nurse scheduling problem (NSP) is proposed. The smart and efficient scheduling procedures were considered. Computation of the performance of each potential solution or schedule was done through fitness evaluation. The best so far solution was obtained via special Maximax&Maximin (MM) parent selection operator embedded in the EA, which fulfilled all constraints considered in the NSP.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Two possible driving forces supporting the evolution of animal communication. Comment on "Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain" by Michael A. Arbib

NASA Astrophysics Data System (ADS)

Moulin-Frier, Clément; Verschure, Paul F. M. J.

2016-03-01

In the target paper [1], M.A. Arbib proposes a quite exhaustive review of the (often computational) models developed during the last decades that support his detailed scenario on language evolution (the Mirror System Hypothesis, MSH). The approach considers that language evolved from a mirror system for grasping already present in LCA-m (the last common ancestor of macaques and humans), to a simple imitation system for grasping present in LCA-c (the last common ancestor of chimpanzees and humans), to a complex imitation system for grasping that developed in the hominid line since that ancestor. MSH considers that this complex imitation system is a key evolutionary step for a language-ready brain, providing all the required elements for an open-ended gestural communication system. The transition from the gestural (bracchio-manual and visual) to the vocal (articulatory and auditory) domain is supposed to be a less important evolutionary step.

The optimal design support system for shell components of vehicles using the methods of artificial intelligence

NASA Astrophysics Data System (ADS)

Szczepanik, M.; Poteralski, A.

2016-11-01

The paper is devoted to an application of the evolutionary methods and the finite element method to the optimization of shell structures. Optimization of thickness of a car wheel (shell) by minimization of stress functional is considered. A car wheel geometry is built from three surfaces of revolution: the central surface with the holes destined for the fastening bolts, the surface of the ring of the wheel and the surface connecting the two mentioned earlier. The last one is subjected to the optimization process. The structures are discretized by triangular finite elements and subjected to the volume constraints. Using proposed method, material properties or thickness of finite elements are changing evolutionally and some of them are eliminated. As a result the optimal shape, topology and material or thickness of the structures are obtained. The numerical examples demonstrate that the method based on evolutionary computation is an effective technique for solving computer aided optimal design.

Ontogenetic ritualization of primate gesture as a case study in dyadic brain modeling.

PubMed

Gasser, Brad; Cartmill, Erica A; Arbib, Michael A

2014-01-01

This paper introduces dyadic brain modeling - the simultaneous, computational modeling of the brains of two interacting agents - to explore ways in which our understanding of macaque brain circuitry can ground new models of brain mechanisms involved in ape interaction. Specifically, we assess a range of data on gestural communication of great apes as the basis for developing an account of the interactions of two primates engaged in ontogenetic ritualization, a proposed learning mechanism through which a functional action may become a communicative gesture over repeated interactions between two individuals (the 'dyad'). The integration of behavioral, neural, and computational data in dyadic (or, more generally, social) brain modeling has broad application to comparative and evolutionary questions, particularly for the evolutionary origins of cognition and language in the human lineage. We relate this work to the neuroinformatics challenges of integrating and sharing data to support collaboration between primatologists, neuroscientists and modelers that will help speed the emergence of what may be called comparative neuro-primatology.

Solving multi-objective water management problems using evolutionary computation.

PubMed

Lewis, A; Randall, M

2017-12-15

Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water management will be a valuable contribution. In this paper, evolutionary computation techniques and Pareto optimisation are incorporated in a model-based system for water management. An illustrative test case modelling optimal crop selection across dry, average and wet years based on data from the Murrumbidgee Irrigation Area in Australia is presented. It is shown that sets of trade-off solutions that provide large net revenues, or minimise environmental flow deficits can be produced rapidly, easily and automatically. The system is capable of providing detailed information on optimal solutions to achieve desired outcomes, responding to a variety of factors including climate conditions and economics. Copyright © 2017 Elsevier Ltd. All rights reserved.

A combined analytical formulation and genetic algorithm to analyze the nonlinear damage responses of continuous fiber toughened composites

NASA Astrophysics Data System (ADS)

Jeon, Haemin; Yu, Jaesang; Lee, Hunsu; Kim, G. M.; Kim, Jae Woo; Jung, Yong Chae; Yang, Cheol-Min; Yang, B. J.

2017-09-01

Continuous fiber-reinforced composites are important materials that have the highest commercialized potential in the upcoming future among existing advanced materials. Despite their wide use and value, their theoretical mechanisms have not been fully established due to the complexity of the compositions and their unrevealed failure mechanisms. This study proposes an effective three-dimensional damage modeling of a fibrous composite by combining analytical micromechanics and evolutionary computation. The interface characteristics, debonding damage, and micro-cracks are considered to be the most influential factors on the toughness and failure behaviors of composites, and a constitutive equation considering these factors was explicitly derived in accordance with the micromechanics-based ensemble volume averaged method. The optimal set of various model parameters in the analytical model were found using modified evolutionary computation that considers human-induced error. The effectiveness of the proposed formulation was validated by comparing a series of numerical simulations with experimental data from available studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frolov, T.; Setyawan, W.; Kurtz, R. J.

We report a computational discovery of novel grain boundary structures and multiple grain boundary phases in elemental bcc tungsten. While grain boundary structures created by the - surface method as a union of two perfect half crystals have been studied extensively, it is known that the method has limitations and does not always predict the correct ground states. Here, we use a newly developed computational tool, based on evolutionary algorithms, to perform a grand-canonical search of high-angle symmetric tilt boundary in tungsten, and we find new ground states and multiple phases that cannot be described using the conventional structural unitmore » model. We use MD simulations to demonstrate that the new structures can coexist at finite temperature in a closed system, confirming these are examples of different GB phases. The new ground state is confirmed by first-principles calculations.Evolutionary grand-canonical search predicts novel grain boundary structures and multiple grain boundary phases in elemental body-centered cubic (bcc) metals represented by tungsten, tantalum and molybdenum.« less

A security mechanism based on evolutionary game in fog computing.

PubMed

Sun, Yan; Lin, Fuhong; Zhang, Nan

2018-02-01

Fog computing is a distributed computing paradigm at the edge of the network and requires cooperation of users and sharing of resources. When users in fog computing open their resources, their devices are easily intercepted and attacked because they are accessed through wireless network and present an extensive geographical distribution. In this study, a credible third party was introduced to supervise the behavior of users and protect the security of user cooperation. A fog computing security mechanism based on human nervous system is proposed, and the strategy for a stable system evolution is calculated. The MATLAB simulation results show that the proposed mechanism can reduce the number of attack behaviors effectively and stimulate users to cooperate in application tasks positively.

Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

PubMed

Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

2017-10-01

While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

Divergent evolutionary processes associated with colonization of offshore islands.

PubMed

Martínková, Natália; Barnett, Ross; Cucchi, Thomas; Struchen, Rahel; Pascal, Marine; Pascal, Michel; Fischer, Martin C; Higham, Thomas; Brace, Selina; Ho, Simon Y W; Quéré, Jean-Pierre; O'Higgins, Paul; Excoffier, Laurent; Heckel, Gerald; Hoelzel, A Rus; Dobney, Keith M; Searle, Jeremy B

2013-10-01

Oceanic islands have been a test ground for evolutionary theory, but here, we focus on the possibilities for evolutionary study created by offshore islands. These can be colonized through various means and by a wide range of species, including those with low dispersal capabilities. We use morphology, modern and ancient sequences of cytochrome b (cytb) and microsatellite genotypes to examine colonization history and evolutionary change associated with occupation of the Orkney archipelago by the common vole (Microtus arvalis), a species found in continental Europe but not in Britain. Among possible colonization scenarios, our results are most consistent with human introduction at least 5100 bp (confirmed by radiocarbon dating). We used approximate Bayesian computation of population history to infer the coast of Belgium as the possible source and estimated the evolutionary timescale using a Bayesian coalescent approach. We showed substantial morphological divergence of the island populations, including a size increase presumably driven by selection and reduced microsatellite variation likely reflecting founder events and genetic drift. More surprisingly, our results suggest that a recent and widespread cytb replacement event in the continental source area purged cytb variation there, whereas the ancestral diversity is largely retained in the colonized islands as a genetic 'ark'. The replacement event in the continental M. arvalis was probably triggered by anthropogenic causes (land-use change). Our studies illustrate that small offshore islands can act as field laboratories for studying various evolutionary processes over relatively short timescales, informing about the mainland source area as well as the island. © 2013 John Wiley & Sons Ltd.

Estimating the ratios of the stationary distribution values for Markov chains modeling evolutionary algorithms.

PubMed

Mitavskiy, Boris; Cannings, Chris

2009-01-01

The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.

A real negative selection algorithm with evolutionary preference for anomaly detection

NASA Astrophysics Data System (ADS)

Yang, Tao; Chen, Wen; Li, Tao

2017-04-01

Traditional real negative selection algorithms (RNSAs) adopt the estimated coverage (c0) as the algorithm termination threshold, and generate detectors randomly. With increasing dimensions, the data samples could reside in the low-dimensional subspace, so that the traditional detectors cannot effectively distinguish these samples. Furthermore, in high-dimensional feature space, c0 cannot exactly reflect the detectors set coverage rate for the nonself space, and it could lead the algorithm to be terminated unexpectedly when the number of detectors is insufficient. These shortcomings make the traditional RNSAs to perform poorly in high-dimensional feature space. Based upon "evolutionary preference" theory in immunology, this paper presents a real negative selection algorithm with evolutionary preference (RNSAP). RNSAP utilizes the "unknown nonself space", "low-dimensional target subspace" and "known nonself feature" as the evolutionary preference to guide the generation of detectors, thus ensuring the detectors can cover the nonself space more effectively. Besides, RNSAP uses redundancy to replace c0 as the termination threshold, in this way RNSAP can generate adequate detectors under a proper convergence rate. The theoretical analysis and experimental result demonstrate that, compared to the classical RNSA (V-detector), RNSAP can achieve a higher detection rate, but with less detectors and computing cost.

Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

NASA Astrophysics Data System (ADS)

Kowalczuk, Zdzisław; Białaszewski, Tomasz

2018-01-01

A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.

Evolution of Swarming Behavior Is Shaped by How Predators Attack.

PubMed

Olson, Randal S; Knoester, David B; Adami, Christoph

2016-01-01

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of domains of danger. Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

Improved Evolutionary Programming with Various Crossover Techniques for Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Tangpatiphan, Kritsana; Yokoyama, Akihiko

This paper presents an Improved Evolutionary Programming (IEP) for solving the Optimal Power Flow (OPF) problem, which is considered as a non-linear, non-smooth, and multimodal optimization problem in power system operation. The total generator fuel cost is regarded as an objective function to be minimized. The proposed method is an Evolutionary Programming (EP)-based algorithm with making use of various crossover techniques, normally applied in Real Coded Genetic Algorithm (RCGA). The effectiveness of the proposed approach is investigated on the IEEE 30-bus system with three different types of fuel cost functions; namely the quadratic cost curve, the piecewise quadratic cost curve, and the quadratic cost curve superimposed by sine component. These three cost curves represent the generator fuel cost functions with a simplified model and more accurate models of a combined-cycle generating unit and a thermal unit with value-point loading effect respectively. The OPF solutions by the proposed method and Pure Evolutionary Programming (PEP) are observed and compared. The simulation results indicate that IEP requires less computing time than PEP with better solutions in some cases. Moreover, the influences of important IEP parameters on the OPF solution are described in details.

Tools for Accurate and Efficient Analysis of Complex Evolutionary Mechanisms in Microbial Genomes. Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakhleh, Luay

I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbialmore » genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.« less

Synthetic transitions: towards a new synthesis

PubMed Central

Solé, Ricard

2016-01-01

The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics. This article is part of the themed issue ‘The major synthetic evolutionary transitions’. PMID:27431516

Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and a-Posteriori Error Estimation Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estep, Donald

2015-11-30

This project addressed the challenge of predictive computational analysis of strongly coupled, highly nonlinear multiphysics systems characterized by multiple physical phenomena that span a large range of length- and time-scales. Specifically, the project was focused on computational estimation of numerical error and sensitivity analysis of computational solutions with respect to variations in parameters and data. In addition, the project investigated the use of accurate computational estimates to guide efficient adaptive discretization. The project developed, analyzed and evaluated new variational adjoint-based techniques for integration, model, and data error estimation/control and sensitivity analysis, in evolutionary multiphysics multiscale simulations.

Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

NASA Astrophysics Data System (ADS)

Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

2011-06-01

Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

Indexing the Comics: A Librarian's Perspective on Comics Research.

ERIC Educational Resources Information Center

Scott, Randall W.

The potential for computers in indexing popular fiction study materials is discussed, and specific examples of comic book indexing are provided through descriptions of projects and a bibliography. The 4-stage evolutionary development of popular fiction studies includes: (1) discovery and reading; (2) bibliography and collecting; (3) cataloging and…

Three Essays on Digital Evolution

ERIC Educational Resources Information Center

Zhang, Zhewei

2016-01-01

Digital products are rapidly shaping our world into a ubiquitous computing world. Because of its unique characteristics, digital artifacts are generative and highly evolving through the recombination of existing elements as well as by the invention of new elements. In this thesis, I first propose an evolutionary view to examine how digital…

Evolving Agents as a Metaphor for the Developing Child

ERIC Educational Resources Information Center

Schlesinger, Matthew

2004-01-01

The emerging field of Evolutionary Computation (EC), inspired by neo-Darwinian principles (e.g. natural selection, mutation, etc.), offers developmental psychologists a wide array of mathematical tools for simulating ontogenetic processes. In this brief review, I begin by highlighting three of the approaches that EC researchers employ (Artificial…

Bioinformatics: A History of Evolution "In Silico"

ERIC Educational Resources Information Center

Ondrej, Vladan; Dvorak, Petr

2012-01-01

Bioinformatics, biological databases, and the worldwide use of computers have accelerated biological research in many fields, such as evolutionary biology. Here, we describe a primer of nucleotide sequence management and the construction of a phylogenetic tree with two examples; the two selected are from completely different groups of organisms:…

Understanding Phylogenies in Biology: The Influence of a Gestalt Perceptual Principle

ERIC Educational Resources Information Center

Novick, Laura R.; Catley, Kefyn M.

2007-01-01

Cladograms, hierarchical diagrams depicting evolutionary histories among (groups of) species, are commonly drawn in 2 informationally equivalent formats--tree and ladder. The authors hypothesize that these formats are not computationally equivalent because the Gestalt principle of good continuation obscures the hierarchical structure of ladders.…

The Myth of Digital Nirvana.

ERIC Educational Resources Information Center

Bennahum, David S.

1996-01-01

Although some see cyberspace as a transcendent medium that will naturally and inevitably usher in a Golden Age, allowing us to ascend to a higher plane of consciousness, the history of computer science refutes this myth. Instead of being the product of an evolutionary process, cyberspace has been deliberately designed by individual people. (PEN)

Phylogenetics Exercise Using Inherited Human Traits

ERIC Educational Resources Information Center

Tuimala, Jarno

2006-01-01

A bioinformatics laboratory exercise based on inherited human morphological traits is presented. It teaches how morphological characters can be used to study the evolutionary history of humans using parsimony. The exercise can easily be used in a pen-and-paper laboratory, but if computers are available, a more versatile analysis can be carried…

Evolutionary Scheduler for the Deep Space Network

NASA Technical Reports Server (NTRS)

Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

2010-01-01

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

The Structure of Phonological Theory

ERIC Educational Resources Information Center

Samuels, Bridget D.

2009-01-01

This dissertation takes a Minimalist approach to phonology, treating the phonological module as a system of abstract symbolic computation, divorced from phonetic content. I investigate the position of the phonological module within the architecture of grammar and the evolutionary scenario developed by Hauser et al. (2002a) and Fitch et al. (2005).…

Generative Representations for Automated Design of Robots

NASA Technical Reports Server (NTRS)

Homby, Gregory S.; Lipson, Hod; Pollack, Jordan B.

2007-01-01

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term generative representations as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity. Automated design of robots through synthetic evolutionary processes has already been demonstrated, but it is not clear whether genetically inspired search algorithms can yield designs that are sufficiently complex for practical engineering. The ultimate success of such algorithms as tools for automation of design depends on the scaling properties of representations of designs. A nongenerative representation (one in which each element of the encoded design is used at most once in translating to the design) scales linearly with the number of elements. Search algorithms that use nongenerative representations quickly become intractable (search times vary approximately exponentially with numbers of design elements), and thus are not amenable to scaling to complex designs. Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps: 1. Generative representations are generated in an evolutionary subprocess. 2. Each generative representation is a program that, when compiled, produces an assembly procedure. 3. In a computational simulation, a constructor executes an assembly procedure to generate a robot. 4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration. In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation. This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations.

Labels, cognomes, and cyclic computation: an ethological perspective.

PubMed

Murphy, Elliot

2015-01-01

For the past two decades, it has widely been assumed by linguists that there is a single computational operation, Merge, which is unique to language, distinguishing it from other cognitive domains. The intention of this paper is to progress the discussion of language evolution in two ways: (i) survey what the ethological record reveals about the uniqueness of the human computational system, and (ii) explore how syntactic theories account for what ethology may determine to be human-specific. It is shown that the operation Label, not Merge, constitutes the evolutionary novelty which distinguishes human language from non-human computational systems; a proposal lending weight to a Weak Continuity Hypothesis and leading to the formation of what is termed Computational Ethology. Some directions for future ethological research are suggested.

IMACS 󈨟: Proceedings of the IMACS World Congress on Computation and Applied Mathematics (13th) Held in Dublin, Ireland on July 22-26, 1991. Volume 2. Computational Fluid Dynamics and Wave Propagation, Parallel Computing, Concurrent and Supercomputing, Computational Physics/Computational Chemistry and Evolutionary Systems

DTIC Science & Technology

1991-01-01

Investigations of uniform convergence Sorption gel5ster Stoffe in porisen Medien (in German). Ver- are-in progress. lag P -Lang, Frankfurt/M., 1991 (in press...ad- sorption terms. Numerical results for solute transport with instantaneous, /it + 01(p): - q(p)#= 0, X > 0, t > 0. (5) nonlinear adsorption-are...13 ] A, S113S = (PfIJl) 8Iax, S1136= m A, y H137=- pf A, v 138 -f A, IIso l r opic-visco° c ousic- Y 139 im A, z-pliole (C’ilz) y 1141 = [ PfA 𔃻 + P

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

NASA Astrophysics Data System (ADS)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Labels, cognomes, and cyclic computation: an ethological perspective

PubMed Central

Murphy, Elliot

2015-01-01

For the past two decades, it has widely been assumed by linguists that there is a single computational operation, Merge, which is unique to language, distinguishing it from other cognitive domains. The intention of this paper is to progress the discussion of language evolution in two ways: (i) survey what the ethological record reveals about the uniqueness of the human computational system, and (ii) explore how syntactic theories account for what ethology may determine to be human-specific. It is shown that the operation Label, not Merge, constitutes the evolutionary novelty which distinguishes human language from non-human computational systems; a proposal lending weight to a Weak Continuity Hypothesis and leading to the formation of what is termed Computational Ethology. Some directions for future ethological research are suggested. PMID:26089809

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Bridging the physical scales in evolutionary biology: From protein sequence space to fitness of organisms and populations

PubMed Central

Bershtein, Shimon; Serohijos, Adrian W.R.; Shakhnovich, Eugene I.

2016-01-01

Bridging the gap between the molecular properties of proteins and organismal/population fitness is essential for understanding evolutionary processes. This task requires the integration of the several physical scales of biological organization, each defined by a distinct set of mechanisms and constraints, into a single unifying model. The molecular scale is dominated by the constraints imposed by the physico-chemical properties of proteins and their substrates, which give rise to trade-offs and epistatic (non-additive) effects of mutations. At the systems scale, biological networks modulate protein expression and can either buffer or enhance the fitness effects of mutations. The population scale is influenced by the mutational input, selection regimes, and stochastic changes affecting the size and structure of populations, which eventually determine the evolutionary fate of mutations. Here, we summarize the recent advances in theory, computer simulations, and experiments that advance our understanding of the links between various physical scales in biology. PMID:27810574

Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations.

PubMed

Bershtein, Shimon; Serohijos, Adrian Wr; Shakhnovich, Eugene I

2017-02-01

Bridging the gap between the molecular properties of proteins and organismal/population fitness is essential for understanding evolutionary processes. This task requires the integration of the several physical scales of biological organization, each defined by a distinct set of mechanisms and constraints, into a single unifying model. The molecular scale is dominated by the constraints imposed by the physico-chemical properties of proteins and their substrates, which give rise to trade-offs and epistatic (non-additive) effects of mutations. At the systems scale, biological networks modulate protein expression and can either buffer or enhance the fitness effects of mutations. The population scale is influenced by the mutational input, selection regimes, and stochastic changes affecting the size and structure of populations, which eventually determine the evolutionary fate of mutations. Here, we summarize the recent advances in theory, computer simulations, and experiments that advance our understanding of the links between various physical scales in biology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Smartphones in ecology and evolution: a guide for the app-rehensive.

PubMed

Teacher, Amber G F; Griffiths, David J; Hodgson, David J; Inger, Richard

2013-12-01

Smartphones and their apps (application software) are now used by millions of people worldwide and represent a powerful combination of sensors, information transfer, and computing power that deserves better exploitation by ecological and evolutionary researchers. We outline the development process for research apps, provide contrasting case studies for two new research apps, and scan the research horizon to suggest how apps can contribute to the rapid collection, interpretation, and dissemination of data in ecology and evolutionary biology. We emphasize that the usefulness of an app relies heavily on the development process, recommend that app developers are engaged with the process at the earliest possible stage, and commend efforts to create open-source software scaffolds on which customized apps can be built by nonexperts. We conclude that smartphones and their apps could replace many traditional handheld sensors, calculators, and data storage devices in ecological and evolutionary research. We identify their potential use in the high-throughput collection, analysis, and storage of complex ecological information.

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

PubMed

Mishra, Bud; Daruwala, Raoul-Sam; Zhou, Yi; Ugel, Nadia; Policriti, Alberto; Antoniotti, Marco; Paxia, Salvatore; Rejali, Marc; Rudra, Archisman; Cherepinsky, Vera; Silver, Naomi; Casey, William; Piazza, Carla; Simeoni, Marta; Barbano, Paolo; Spivak, Marina; Feng, Jiawu; Gill, Ofer; Venkatesh, Mysore; Cheng, Fang; Sun, Bing; Ioniata, Iuliana; Anantharaman, Thomas; Hubbard, E Jane Albert; Pnueli, Amir; Harel, David; Chandru, Vijay; Hariharan, Ramesh; Wigler, Michael; Park, Frank; Lin, Shih-Chieh; Lazebnik, Yuri; Winkler, Franz; Cantor, Charles R; Carbone, Alessandra; Gromov, Mikhael

2003-01-01

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.

Heterogeneous Compression of Large Collections of Evolutionary Trees.

PubMed

Matthews, Suzanne J

2015-01-01

Compressing heterogeneous collections of trees is an open problem in computational phylogenetics. In a heterogeneous tree collection, each tree can contain a unique set of taxa. An ideal compression method would allow for the efficient archival of large tree collections and enable scientists to identify common evolutionary relationships over disparate analyses. In this paper, we extend TreeZip to compress heterogeneous collections of trees. TreeZip is the most efficient algorithm for compressing homogeneous tree collections. To the best of our knowledge, no other domain-based compression algorithm exists for large heterogeneous tree collections or enable their rapid analysis. Our experimental results indicate that TreeZip averages 89.03 percent (72.69 percent) space savings on unweighted (weighted) collections of trees when the level of heterogeneity in a collection is moderate. The organization of the TRZ file allows for efficient computations over heterogeneous data. For example, consensus trees can be computed in mere seconds. Lastly, combining the TreeZip compressed (TRZ) file with general-purpose compression yields average space savings of 97.34 percent (81.43 percent) on unweighted (weighted) collections of trees. Our results lead us to believe that TreeZip will prove invaluable in the efficient archival of tree collections, and enables scientists to develop novel methods for relating heterogeneous collections of trees.

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains.

PubMed

Muthu Krishnan, S

2018-05-14

The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through the structural and sequence-based analysis, found various protein folds that are very close to the RAP folds. Remote homolog datasets were used potentially to develop a different support vector machine (SVM) methods to recognize the homologous RAP fold. This study helps in understanding the relationship of RAP homologs folds based on the structure, function and evolutionary history. Copyright © 2018 Elsevier Ltd. All rights reserved.

Developmental and Evolutionary Lexicon Acquisition in Cognitive Agents/Robots with Grounding Principle: A Short Review.

PubMed

Rasheed, Nadia; Amin, Shamsudin H M

2016-01-01

Grounded language acquisition is an important issue, particularly to facilitate human-robot interactions in an intelligent and effective way. The evolutionary and developmental language acquisition are two innovative and important methodologies for the grounding of language in cognitive agents or robots, the aim of which is to address current limitations in robot design. This paper concentrates on these two main modelling methods with the grounding principle for the acquisition of linguistic ability in cognitive agents or robots. This review not only presents a survey of the methodologies and relevant computational cognitive agents or robotic models, but also highlights the advantages and progress of these approaches for the language grounding issue.

Developmental and Evolutionary Lexicon Acquisition in Cognitive Agents/Robots with Grounding Principle: A Short Review

PubMed Central

Rasheed, Nadia; Amin, Shamsudin H. M.

2016-01-01

Grounded language acquisition is an important issue, particularly to facilitate human-robot interactions in an intelligent and effective way. The evolutionary and developmental language acquisition are two innovative and important methodologies for the grounding of language in cognitive agents or robots, the aim of which is to address current limitations in robot design. This paper concentrates on these two main modelling methods with the grounding principle for the acquisition of linguistic ability in cognitive agents or robots. This review not only presents a survey of the methodologies and relevant computational cognitive agents or robotic models, but also highlights the advantages and progress of these approaches for the language grounding issue. PMID:27069470

An Evolutionary Comparison of the Handicap Principle and Hybrid Equilibrium Theories of Signaling

PubMed Central

Kane, Patrick; Zollman, Kevin J. S.

2015-01-01

The handicap principle has come under significant challenge both from empirical studies and from theoretical work. As a result, a number of alternative explanations for honest signaling have been proposed. This paper compares the evolutionary plausibility of one such alternative, the “hybrid equilibrium,” to the handicap principle. We utilize computer simulations to compare these two theories as they are instantiated in Maynard Smith’s Sir Philip Sidney game. We conclude that, when both types of communication are possible, evolution is unlikely to lead to handicap signaling and is far more likely to result in the partially honest signaling predicted by hybrid equilibrium theory. PMID:26348617

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Design Mining Interacting Wind Turbines.

PubMed

Preen, Richard J; Bull, Larry

2016-01-01

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles

NASA Astrophysics Data System (ADS)

Kolsbjerg, E. L.; Peterson, A. A.; Hammer, B.

2018-05-01

We show that approximate structural relaxation with a neural network enables orders of magnitude faster global optimization with an evolutionary algorithm in a density functional theory framework. The increased speed facilitates reliable identification of global minimum energy structures, as exemplified by our finding of a hollow Pt13 nanoparticle on an MgO support. We highlight the importance of knowing the correct structure when studying the catalytic reactivity of the different particle shapes. The computational speedup further enables screening of hundreds of different pathways in the search for optimum kinetic transitions between low-energy conformers and hence pushes the limits of the insight into thermal ensembles that can be obtained from theory.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Rooting phylogenetic trees under the coalescent model using site pattern probabilities.

PubMed

Tian, Yuan; Kubatko, Laura

2017-12-19

Phylogenetic tree inference is a fundamental tool to estimate ancestor-descendant relationships among different species. In phylogenetic studies, identification of the root - the most recent common ancestor of all sampled organisms - is essential for complete understanding of the evolutionary relationships. Rooted trees benefit most downstream application of phylogenies such as species classification or study of adaptation. Often, trees can be rooted by using outgroups, which are species that are known to be more distantly related to the sampled organisms than any other species in the phylogeny. However, outgroups are not always available in evolutionary research. In this study, we develop a new method for rooting species tree under the coalescent model, by developing a series of hypothesis tests for rooting quartet phylogenies using site pattern probabilities. The power of this method is examined by simulation studies and by application to an empirical North American rattlesnake data set. The method shows high accuracy across the simulation conditions considered, and performs well for the rattlesnake data. Thus, it provides a computationally efficient way to accurately root species-level phylogenies that incorporates the coalescent process. The method is robust to variation in substitution model, but is sensitive to the assumption of a molecular clock. Our study establishes a computationally practical method for rooting species trees that is more efficient than traditional methods. The method will benefit numerous evolutionary studies that require rooting a phylogenetic tree without having to specify outgroups.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PubMed

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

On evolutionary climate tracks in deep mantle volatile cycle computed from numerical mantle convection simulations and its impact on the habitability of the Earth-like planets

NASA Astrophysics Data System (ADS)

Nakagawa, T.; Tajika, E.; Kadoya, S.

2017-12-01

Discussing an impact of evolution and dynamics in the Earth's deep interior on the surface climate change for the last few decades (see review by Ehlmann et al., 2016), the mantle volatile (particularly carbon) degassing in the mid-oceanic ridges seems to play a key role in understanding the evolutionary climate track for Earth-like planets (e.g. Kadoya and Tajika, 2015). However, since the mantle degassing occurs not only in the mid-oceanic ridges but also in the wedge mantle (island arc volcanism) and hotspots, to incorporate more accurate estimate of mantle degassing flux into the climate evolution framework, we developed a coupled model of surface climate-deep Earth evolution in numerical mantle convection simulations, including more accurate deep water and carbon cycle (e.g. Nakagawa and Spiegelman, 2017) with an energy balance theory of climate change. Modeling results suggest that the evolution of planetary climate computed from a developed model is basically consistent with an evolutionary climate track in simplified mantle degassing model (Kadoya and Tajika, 2015), but an occurrence timing of global (snowball) glaciation is strongly dependent on mantle degassing rate occurred with activities of surface plate motions. With this implication, the surface plate motion driven by deep mantle dynamics would play an important role in the planetary habitability of such as the Earth and Earth-like planets over geologic time-scale.

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

PubMed

Branke, Jürgen; Hildebrandt, Torsten; Scholz-Reiter, Bernd

2015-01-01

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

The State of Software for Evolutionary Biology.

PubMed

Darriba, Diego; Flouri, Tomáš; Stamatakis, Alexandros

2018-05-01

With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C++ (e.g., MrBayes, MAFFT, SweepFinder, etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines. Because, the software engineering quality of the tools we analyzed is rather unsatisfying, we provide a list of best practices for improving the quality of existing tools and list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal as well as science policy and, more importantly, funding issues that need to be addressed for improving software engineering quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software engineering quality issues and to emphasize the substantial lack of funding for scientific software development.

ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements.

PubMed

Taylor, James; Tyekucheva, Svitlana; King, David C; Hardison, Ross C; Miller, Webb; Chiaromonte, Francesca

2006-12-01

Genomic sequence signals - such as base composition, presence of particular motifs, or evolutionary constraint - have been used effectively to identify functional elements. However, approaches based only on specific signals known to correlate with function can be quite limiting. When training data are available, application of computational learning algorithms to multispecies alignments has the potential to capture broader and more informative sequence and evolutionary patterns that better characterize a class of elements. However, effective exploitation of patterns in multispecies alignments is impeded by the vast number of possible alignment columns and by a limited understanding of which particular strings of columns may characterize a given class. We have developed a computational method, called ESPERR (evolutionary and sequence pattern extraction through reduced representations), which uses training examples to learn encodings of multispecies alignments into reduced forms tailored for the prediction of chosen classes of functional elements. ESPERR produces a greatly improved Regulatory Potential score, which can discriminate regulatory regions from neutral sites with excellent accuracy ( approximately 94%). This score captures strong signals (GC content and conservation), as well as subtler signals (with small contributions from many different alignment patterns) that characterize the regulatory elements in our training set. ESPERR is also effective for predicting other classes of functional elements, as we show for DNaseI hypersensitive sites and highly conserved regions with developmental enhancer activity. Our software, training data, and genome-wide predictions are available from our Web site (http://www.bx.psu.edu/projects/esperr).

A biologically consistent hierarchical framework for self-referencing survivalist computation

NASA Astrophysics Data System (ADS)

Cottam, Ron; Ranson, Willy; Vounckx, Roger

2000-05-01

Extensively scaled formally rational hardware and software are indirectly fallible, at the very least through temporal restrictions on the evaluation of their correctness. In addition, the apparent inability of formal rationality to successfully describe living systems as anything other than inanimate structures suggests that the development of self-referencing computational machines will require a different approach. There is currently a strong movement towards the adoption of semiotics as a descriptive medium in theoretical biology. We present a related computational semiosic construction (1, 2) consistent with evolutionary hierarchical emergence (3), which may serve as a framework for implementing anticipatory-oriented survivalist processing in real environments.

Estimating true evolutionary distances under the DCJ model.

PubMed

Lin, Yu; Moret, Bernard M E

2008-07-01

Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome; such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes? We present a method to estimate the true evolutionary distance between two genomes under the 'double-cut-and-join' (DCJ) model of genome rearrangement, a model under which a single multichromosomal operation accounts for all genomic rearrangement events: inversion, transposition, translocation, block interchange and chromosomal fusion and fission. Our method relies on a simple structural characterization of a genome pair and is both analytically and computationally tractable. We provide analytical results to describe the asymptotic behavior of genomes under the DCJ model, as well as experimental results on a wide variety of genome structures to exemplify the very high accuracy (and low variance) of our estimator. Our results provide a tool for accurate phylogenetic reconstruction from multichromosomal gene rearrangement data as well as a theoretical basis for refinements of the DCJ model to account for biological constraints. All of our software is available in source form under GPL at http://lcbb.epfl.ch.

Computer vision cracks the leaf code

PubMed Central

Wilf, Peter; Zhang, Shengping; Chikkerur, Sharat; Little, Stefan A.; Wing, Scott L.; Serre, Thomas

2016-01-01

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. PMID:26951664

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

NASA Technical Reports Server (NTRS)

Knasel, T. Michael

1996-01-01

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

An experimental and computational evolution-based method to study a mode of co-evolution of overlapping open reading frames in the AAV2 viral genome.

PubMed

Kawano, Yasuhiro; Neeley, Shane; Adachi, Kei; Nakai, Hiroyuki

2013-01-01

Overlapping open reading frames (ORFs) in viral genomes undergo co-evolution; however, how individual amino acids coded by overlapping ORFs are structurally, functionally, and co-evolutionarily constrained remains difficult to address by conventional homologous sequence alignment approaches. We report here a new experimental and computational evolution-based methodology to address this question and report its preliminary application to elucidating a mode of co-evolution of the frame-shifted overlapping ORFs in the adeno-associated virus (AAV) serotype 2 viral genome. These ORFs encode both capsid VP protein and non-structural assembly-activating protein (AAP). To show proof of principle of the new method, we focused on the evolutionarily conserved QVKEVTQ and KSKRSRR motifs, a pair of overlapping heptapeptides in VP and AAP, respectively. In the new method, we first identified a large number of capsid-forming VP3 mutants and functionally competent AAP mutants of these motifs from mutant libraries by experimental directed evolution under no co-evolutionary constraints. We used Illumina sequencing to obtain a large dataset and then statistically assessed the viability of VP and AAP heptapeptide mutants. The obtained heptapeptide information was then integrated into an evolutionary algorithm, with which VP and AAP were co-evolved from random or native nucleotide sequences in silico. As a result, we demonstrate that these two heptapeptide motifs could exhibit high degeneracy if coded by separate nucleotide sequences, and elucidate how overlap-evoked co-evolutionary constraints play a role in making the VP and AAP heptapeptide sequences into the present shape. Specifically, we demonstrate that two valine (V) residues and β-strand propensity in QVKEVTQ are structurally important, the strongly negative and hydrophilic nature of KSKRSRR is functionally important, and overlap-evoked co-evolution imposes strong constraints on serine (S) residues in KSKRSRR, despite high degeneracy of the motifs in the absence of co-evolutionary constraints.

The current evolution of complex high mountain debris-covered glacier systems and its relation with ground ice nature and distribution: the case of Rognes and Pierre Ronde area (Mont-Blanc range, France).

NASA Astrophysics Data System (ADS)

Bosson, Jean-Baptiste; Lambiel, Christophe

2014-05-01

The current climate forcing, through negative glacier mass balance and rockfall intensification, is leading to the rapid burring of many small glacier systems. When the debris mantle exceeds some centimeters of thickness, the climate control on ice melt is mitigated and delayed. As well, debris-covered glaciers respond to climate forcing in a complex way. This situation is emphasised in high mountain environments, where topo-climatic conditions, such as cold temperatures, amount of solid precipitation, duration of snow cover, nebulosity or shadow effect of rockwalls, limit the influence of rising air temperatures in the ground. Beside, due to Holocene climate history, glacier-permafrost interactions are not rare within the periglacial belt. Glacier recurrence may have removed and assimilated former ice-cemented sediments, the negative mass balance may have led to the formation of ice-cored rock glaciers and neopermafrost may have formed recently under cold climate conditions. Hence, in addition to sedimentary ice, high mountain debris-covered glacier systems can contain interstitial magmatic ice. Especially because of their position at the top of alpine cascade systems and of the amount of water and (unconsolidated) sediment involved, it is important to understand and anticipate the evolution of these complex landforms. Due to the continuous and thick debris mantle and to the common existence of dead ice in deglaciated areas, the current extent of debris-covered glacier can be difficult to point out. Thus, the whole system, according to Little Ice Age (LIA) extent, has sometimes to be investigated to understand the current response of glacier systems to the climate warming. In this context, two neighbouring sites, Rognes and Pierre Ronde systems (45°51'38''N, 6°48'40''E; 2600-3100m a.s.l), have been studied since 2011. These sites are almost completely debris-covered and only few ice outcrops in the upper slopes still witness the existence of former glaciers. Electrical resistivity tomographies, kinematic data and ground surface temperature show that heterogeneous responses to climate forcing are occurring despites their small areas (> 0.3 km2). This complex situation is related to Holocene climate history and especially to glacier systems evolution since LIA. The current dynamics depend of ground ice nature and distribution. Five main behaviours can be highlighted: - Debris covered glacier areas are the most active. Their responses to climate forcing are relatively fast, especially through massive ice melt-out each summer. - Ice-cored rock glacier areas are quite active. The existence of massive glacier ice under few meters of debris explain the important surface lowering during the snow free period . - Ice-cemented rock glacier areas are characterised by winter and summer subhorizontal downslope creeping. - Moraine areas containing dead ice have heterogeneous activities (directions and values of detected movements) related to the ice vanishing. - Deglaciated moraine areas are almost inactive, except modest superficial paraglacial rebalancing.

Network Analysis of Plasmidomes: The Azospirillum brasilense Sp245 Case

PubMed Central

Fondi, Marco

2014-01-01

Azospirillum brasilense is a nitrogen-fixing bacterium living in association with plant roots. The genome of the strain Sp245, isolated in Brazil from wheat roots, consists of one chromosome and six plasmids. In this work, the A. brasilense Sp245 plasmids were analyzed in order to shed some light on the evolutionary pathways they followed over time. To this purpose, a similarity network approach was applied in order to identify the evolutionary relationships among all the A. brasilense plasmids encoded proteins; in this context a computational pipeline specifically devoted to the analysis and the visualization of the network-like evolutionary relationships among different plasmids molecules was developed. This information was supplemented with a detailed (in silico) functional characterization of both the connected (i.e., sharing homology with other sequences in the dataset) and the unconnected (i.e., not sharing homology) components of the network. Furthermore, the most likely source organism for each of the genes encoded by A. brasilense plasmids was checked, allowing the identification of possible trends of gene loss/gain in this microorganism. Data obtained provided a detailed description of the evolutionary landscape of the plasmids of A. brasilense Sp245, suggesting some of the molecular mechanisms responsible for the present-day structure of these molecules. PMID:25610702

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

PubMed Central

2018-01-01

The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford's law, fractal dimension, global contrast factor, and Shannon's entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art. PMID:29805440

Institutional Expansion: The Case of Grid Computing

ERIC Educational Resources Information Center

Kertcher, Zack

2010-01-01

Evolutionary and revolutionary approaches have dominated the study of scientific, technological and institutional change. Yet, being focused on change within a single field, these approaches have been mute about a third, pervasive process. This process is found in a variety of cases that range from open source software to the Monte Carlo method to…

Evolutionary in Technology, Revolutionary in Impact

ERIC Educational Resources Information Center

Grush, Mary

2007-01-01

Ken Klingenstein has led national networking initiatives for the past 25 years. He served as director of computing and network services at the University of Colorado at Boulder from 1985-1999, and today, Klingenstein is director of middleware and security for Internet2. Truth is, this networking innovator has participated in the development of the…

Supersmart Robots: The Next Generation of Robots Has Evolutionary Capabilities

ERIC Educational Resources Information Center

Simkins, Michael

2008-01-01

Robots that can learn new behaviors. Robots that can reproduce themselves. Science fiction? Not anymore. Roboticists at Cornell's Computational Synthesis Lab have developed just such engineered creatures that offer interesting implications for education. The team, headed by Hod Lipson, was intrigued by the question, "How can you get robots to be…

Linguistic Evolution through Language Acquisition: Formal and Computational Models.

ERIC Educational Resources Information Center

Briscoe, Ted, Ed.

This collection of papers examines how children acquire language and how this affects language change over the generations. It proceeds from the basis that it is important to address not only the language faculty per se within the framework of evolutionary theory, but also the origins and subsequent development of languages themselves, suggesting…

Parallel evolutionary computation in bioinformatics applications.

PubMed

Pinho, Jorge; Sobral, João Luis; Rocha, Miguel

2013-05-01

A large number of optimization problems within the field of Bioinformatics require methods able to handle its inherent complexity (e.g. NP-hard problems) and also demand increased computational efforts. In this context, the use of parallel architectures is a necessity. In this work, we propose ParJECoLi, a Java based library that offers a large set of metaheuristic methods (such as Evolutionary Algorithms) and also addresses the issue of its efficient execution on a wide range of parallel architectures. The proposed approach focuses on the easiness of use, making the adaptation to distinct parallel environments (multicore, cluster, grid) transparent to the user. Indeed, this work shows how the development of the optimization library can proceed independently of its adaptation for several architectures, making use of Aspect-Oriented Programming. The pluggable nature of parallelism related modules allows the user to easily configure its environment, adding parallelism modules to the base source code when needed. The performance of the platform is validated with two case studies within biological model optimization. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Phylogenetics beyond biology.

PubMed

Retzlaff, Nancy; Stadler, Peter F

2018-06-21

Evolutionary processes have been described not only in biology but also for a wide range of human cultural activities including languages and law. In contrast to the evolution of DNA or protein sequences, the detailed mechanisms giving rise to the observed evolution-like processes are not or only partially known. The absence of a mechanistic model of evolution implies that it remains unknown how the distances between different taxa have to be quantified. Considering distortions of metric distances, we first show that poor choices of the distance measure can lead to incorrect phylogenetic trees. Based on the well-known fact that phylogenetic inference requires additive metrics, we then show that the correct phylogeny can be computed from a distance matrix [Formula: see text] if there is a monotonic, subadditive function [Formula: see text] such that [Formula: see text] is additive. The required metric-preserving transformation [Formula: see text] can be computed as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process remains elusive.

Combining analysis with optimization at Langley Research Center. An evolutionary process

NASA Technical Reports Server (NTRS)

Rogers, J. L., Jr.

1982-01-01

The evolutionary process of combining analysis and optimization codes was traced with a view toward providing insight into the long term goal of developing the methodology for an integrated, multidisciplinary software system for the concurrent analysis and optimization of aerospace structures. It was traced along the lines of strength sizing, concurrent strength and flutter sizing, and general optimization to define a near-term goal for combining analysis and optimization codes. Development of a modular software system combining general-purpose, state-of-the-art, production-level analysis computer programs for structures, aerodynamics, and aeroelasticity with a state-of-the-art optimization program is required. Incorporation of a modular and flexible structural optimization software system into a state-of-the-art finite element analysis computer program will facilitate this effort. This effort results in the software system used that is controlled with a special-purpose language, communicates with a data management system, and is easily modified for adding new programs and capabilities. A 337 degree-of-freedom finite element model is used in verifying the accuracy of this system.

Data collection and storage in long-term ecological and evolutionary studies: The Mongoose 2000 system.

PubMed

Marshall, Harry H; Griffiths, David J; Mwanguhya, Francis; Businge, Robert; Griffiths, Amber G F; Kyabulima, Solomon; Mwesige, Kenneth; Sanderson, Jennifer L; Thompson, Faye J; Vitikainen, Emma I K; Cant, Michael A

2018-01-01

Studying ecological and evolutionary processes in the natural world often requires research projects to follow multiple individuals in the wild over many years. These projects have provided significant advances but may also be hampered by needing to accurately and efficiently collect and store multiple streams of the data from multiple individuals concurrently. The increase in the availability and sophistication of portable computers (smartphones and tablets) and the applications that run on them has the potential to address many of these data collection and storage issues. In this paper we describe the challenges faced by one such long-term, individual-based research project: the Banded Mongoose Research Project in Uganda. We describe a system we have developed called Mongoose 2000 that utilises the potential of apps and portable computers to meet these challenges. We discuss the benefits and limitations of employing such a system in a long-term research project. The app and source code for the Mongoose 2000 system are freely available and we detail how it might be used to aid data collection and storage in other long-term individual-based projects.

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2006-01-01

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Hybrid soft computing systems for electromyographic signals analysis: a review.

PubMed

Xie, Hong-Bo; Guo, Tianruo; Bai, Siwei; Dokos, Socrates

2014-02-03

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

Hybrid soft computing systems for electromyographic signals analysis: a review

PubMed Central

2014-01-01

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis. PMID:24490979

Assessment of traffic noise levels in urban areas using different soft computing techniques.

PubMed

Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

2016-10-01

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

An Application Development Platform for Neuromorphic Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dean, Mark; Chan, Jason; Daffron, Christopher

2016-01-01

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

NASA Astrophysics Data System (ADS)

Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

2017-11-01

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Evolutionary Approach for Relative Gene Expression Algorithms

PubMed Central

Czajkowski, Marcin

2014-01-01

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space. PMID:24790574

Communication scheme based on evolutionary spatial 2×2 games

NASA Astrophysics Data System (ADS)

Ziaukas, Pranas; Ragulskis, Tautvydas; Ragulskis, Minvydas

2014-06-01

A visual communication scheme based on evolutionary spatial 2×2 games is proposed in this paper. Self-organizing patterns induced by complex interactions between competing individuals are exploited for hiding and transmitting secret visual information. Properties of the proposed communication scheme are discussed in details. It is shown that the hiding capacity of the system (the minimum size of the detectable primitives and the minimum distance between two primitives) is sufficient for the effective transmission of digital dichotomous images. Also, it is demonstrated that the proposed communication scheme is resilient to time backwards, plain image attacks and is highly sensitive to perturbations of private and public keys. Several computational experiments are used to demonstrate the effectiveness of the proposed communication scheme.

Multiobjective optimization of temporal processes.

PubMed

Song, Zhe; Kusiak, Andrew

2010-06-01

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.

Fundamentals and Recent Developments in Approximate Bayesian Computation

PubMed Central

Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka

2017-01-01

Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.] PMID:28175922

Testing for Independence between Evolutionary Processes.

PubMed

Behdenna, Abdelkader; Pothier, Joël; Abby, Sophie S; Lambert, Amaury; Achaz, Guillaume

2016-09-01

Evolutionary events co-occurring along phylogenetic trees usually point to complex adaptive phenomena, sometimes implicating epistasis. While a number of methods have been developed to account for co-occurrence of events on the same internal or external branch of an evolutionary tree, there is a need to account for the larger diversity of possible relative positions of events in a tree. Here we propose a method to quantify to what extent two or more evolutionary events are associated on a phylogenetic tree. The method is applicable to any discrete character, like substitutions within a coding sequence or gains/losses of a biological function. Our method uses a general approach to statistically test for significant associations between events along the tree, which encompasses both events inseparable on the same branch, and events genealogically ordered on different branches. It assumes that the phylogeny and themapping of branches is known without errors. We address this problem from the statistical viewpoint by a linear algebra representation of the localization of the evolutionary events on the tree.We compute the full probability distribution of the number of paired events occurring in the same branch or in different branches of the tree, under a null model of independence where each type of event occurs at a constant rate uniformly inthephylogenetic tree. The strengths andweaknesses of themethodare assessed via simulations;we then apply the method to explore the loss of cell motility in intracellular pathogens. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Crowd Computing as a Cooperation Problem: An Evolutionary Approach

NASA Astrophysics Data System (ADS)

Christoforou, Evgenia; Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A.; Sánchez, Angel

2013-05-01

Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. Here, we take a step forward by studying cooperation in the context of crowd computing. We introduce a model loosely based on Principal-agent theory in which people (workers) contribute to the solution of a distributed problem by computing answers and reporting to the problem proposer (master). To go beyond classical approaches involving the concept of Nash equilibrium, we work on an evolutionary framework in which both the master and the workers update their behavior through reinforcement learning. Using a Markov chain approach, we show theoretically that under certain----not very restrictive—conditions, the master can ensure the reliability of the answer resulting of the process. Then, we study the model by numerical simulations, finding that convergence, meaning that the system reaches a point in which it always produces reliable answers, may in general be much faster than the upper bounds given by the theoretical calculation. We also discuss the effects of the master's level of tolerance to defectors, about which the theory does not provide information. The discussion shows that the system works even with very large tolerances. We conclude with a discussion of our results and possible directions to carry this research further.

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

The State of Software for Evolutionary Biology

PubMed Central

Darriba, Diego; Flouri, Tomáš; Stamatakis, Alexandros

2018-01-01

Abstract With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C++ (e.g., MrBayes, MAFFT, SweepFinder, etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines. Because, the software engineering quality of the tools we analyzed is rather unsatisfying, we provide a list of best practices for improving the quality of existing tools and list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal as well as science policy and, more importantly, funding issues that need to be addressed for improving software engineering quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software engineering quality issues and to emphasize the substantial lack of funding for scientific software development. PMID:29385525

PhyloBot: A Web Portal for Automated Phylogenetics, Ancestral Sequence Reconstruction, and Exploration of Mutational Trajectories.

PubMed

Hanson-Smith, Victor; Johnson, Alexander

2016-07-01

The method of phylogenetic ancestral sequence reconstruction is a powerful approach for studying evolutionary relationships among protein sequence, structure, and function. In particular, this approach allows investigators to (1) reconstruct and "resurrect" (that is, synthesize in vivo or in vitro) extinct proteins to study how they differ from modern proteins, (2) identify key amino acid changes that, over evolutionary timescales, have altered the function of the protein, and (3) order historical events in the evolution of protein function. Widespread use of this approach has been slow among molecular biologists, in part because the methods require significant computational expertise. Here we present PhyloBot, a web-based software tool that makes ancestral sequence reconstruction easy. Designed for non-experts, it integrates all the necessary software into a single user interface. Additionally, PhyloBot provides interactive tools to explore evolutionary trajectories between ancestors, enabling the rapid generation of hypotheses that can be tested using genetic or biochemical approaches. Early versions of this software were used in previous studies to discover genetic mechanisms underlying the functions of diverse protein families, including V-ATPase ion pumps, DNA-binding transcription regulators, and serine/threonine protein kinases. PhyloBot runs in a web browser, and is available at the following URL: http://www.phylobot.com. The software is implemented in Python using the Django web framework, and runs on elastic cloud computing resources from Amazon Web Services. Users can create and submit jobs on our free server (at the URL listed above), or use our open-source code to launch their own PhyloBot server.

PhyloBot: A Web Portal for Automated Phylogenetics, Ancestral Sequence Reconstruction, and Exploration of Mutational Trajectories

PubMed Central

Hanson-Smith, Victor; Johnson, Alexander

2016-01-01

The method of phylogenetic ancestral sequence reconstruction is a powerful approach for studying evolutionary relationships among protein sequence, structure, and function. In particular, this approach allows investigators to (1) reconstruct and “resurrect” (that is, synthesize in vivo or in vitro) extinct proteins to study how they differ from modern proteins, (2) identify key amino acid changes that, over evolutionary timescales, have altered the function of the protein, and (3) order historical events in the evolution of protein function. Widespread use of this approach has been slow among molecular biologists, in part because the methods require significant computational expertise. Here we present PhyloBot, a web-based software tool that makes ancestral sequence reconstruction easy. Designed for non-experts, it integrates all the necessary software into a single user interface. Additionally, PhyloBot provides interactive tools to explore evolutionary trajectories between ancestors, enabling the rapid generation of hypotheses that can be tested using genetic or biochemical approaches. Early versions of this software were used in previous studies to discover genetic mechanisms underlying the functions of diverse protein families, including V-ATPase ion pumps, DNA-binding transcription regulators, and serine/threonine protein kinases. PhyloBot runs in a web browser, and is available at the following URL: http://www.phylobot.com. The software is implemented in Python using the Django web framework, and runs on elastic cloud computing resources from Amazon Web Services. Users can create and submit jobs on our free server (at the URL listed above), or use our open-source code to launch their own PhyloBot server. PMID:27472806

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

The G matrix under fluctuating correlational mutation and selection.

PubMed

Revell, Liam J

2007-08-01

Theoretical quantitative genetics provides a framework for reconstructing past selection and predicting future patterns of phenotypic differentiation. However, the usefulness of the equations of quantitative genetics for evolutionary inference relies on the evolutionary stability of the additive genetic variance-covariance matrix (G matrix). A fruitful new approach for exploring the evolutionary dynamics of G involves the use of individual-based computer simulations. Previous studies have focused on the evolution of the eigenstructure of G. An alternative approach employed in this paper uses the multivariate response-to-selection equation to evaluate the stability of G. In this approach, I measure similarity by the correlation between response-to-selection vectors due to random selection gradients. I analyze the dynamics of G under several conditions of correlational mutation and selection. As found in a previous study, the eigenstructure of G is stabilized by correlational mutation and selection. However, over broad conditions, instability of G did not result in a decreased consistency of the response to selection. I also analyze the stability of G when the correlation coefficients of correlational mutation and selection and the effective population size change through time. To my knowledge, no prior study has used computer simulations to investigate the stability of G when correlational mutation and selection fluctuate. Under these conditions, the eigenstructure of G is unstable under some simulation conditions. Different results are obtained if G matrix stability is assessed by eigenanalysis or by the response to random selection gradients. In this case, the response to selection is most consistent when certain aspects of the eigenstructure of G are least stable and vice versa.

Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

NASA Astrophysics Data System (ADS)

Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

2018-01-01

In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

Comment on "Protein sequences from mastodon and Tyrannosaurus rex revealed by mass spectrometry".

PubMed

Pevzner, Pavel A; Kim, Sangtae; Ng, Julio

2008-08-22

Asara et al. (Reports, 13 April 2007, p. 280) reported sequencing of Tyrannosaurus rex proteins and used them to establish the evolutionary relationships between birds and dinosaurs. We argue that the reported T. rex peptides may represent statistical artifacts and call for complete data release to enable experimental and computational verification of their findings.

An Evolutionary Algorithm for Feature Subset Selection in Hard Disk Drive Failure Prediction

ERIC Educational Resources Information Center

Bhasin, Harpreet

2011-01-01

Hard disk drives are used in everyday life to store critical data. Although they are reliable, failure of a hard disk drive can be catastrophic, especially in applications like medicine, banking, air traffic control systems, missile guidance systems, computer numerical controlled machines, and more. The use of Self-Monitoring, Analysis and…

The Evolution of Cartography Graduate Programs and the Development of New Graduate Programs in Cartography: An Assessment of Models.

ERIC Educational Resources Information Center

Steinke, Theodore R.

This paper traces the historical development of cartography graduate programs, establishes an evolutionary model, and evaluates the model to determine if it has some utility today for the development of programs capable of producing highly skilled cartographers. Cartography is defined to include traditional cartography, computer cartography,…

A Computer Lab Exploring Evolutionary Aspects of Chromatin Structure and Dynamics for an Undergraduate Chromatin Course

ERIC Educational Resources Information Center

Eirin-Lopez, Jose M.

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a…

Modelling of RR Lyrae instability strips

NASA Astrophysics Data System (ADS)

Szabo, Robert; Csubry, Zoltan

2001-02-01

Recent studies indicates that the slope of the empirical blue edge of the RR Lyrae fundamental mode instability strip is irreconcilable with the theoretical blue edges. Nonlinear hydrodynamical pulsational code involving turbulent convection was used to follow fundamental/first overtone mode selection mechanism. This method combined with the results of horizontal branch evolutionary computations was applied to rethink the problem.

Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: case study of the invasive ladybird Harmonia axyridis

USDA-ARS?s Scientific Manuscript database

The correct identification of the source population of an invasive species is a prerequisite for defining and testing different hypotheses concerning the environmental and evolutionary factors responsible for biological invasions. The native area of invasive species may be large, barely known and/or...

The Evolutionary Development of CAI Hardware.

ERIC Educational Resources Information Center

Stifle, John E.

After six years of research in computer assisted instruction (CAI) using PLATO III, a decision was made at the University of Illinois to develop a larger system as a national CAI resource. This document describes the design specifications and problems in the development of PLATO IV, a system which is capable of accomodating up to 4,000 terminals…

Ancient Biomolecules and Evolutionary Inference.

PubMed

Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando; Welker, Frido; Pedersen, Mikkel Winther; Allentoft, Morten E; de Barros Damgaard, Peter; Gutenbrunner, Petra; Dunne, Julie; Hammann, Simon; Roffet-Salque, Mélanie; Ilardo, Melissa; Moreno-Mayar, J Víctor; Wang, Yucheng; Sikora, Martin; Vinner, Lasse; Cox, Jürgen; Evershed, Richard P; Willerslev, Eske

2018-04-25

Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field. Expected final online publication date for the Annual Review of Biochemistry Volume 87 is June 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily

PubMed Central

Akiva, Eyal; Copp, Janine N.; Tokuriki, Nobuhiko; Babbitt, Patricia C.

2017-01-01

Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold. PMID:29078300

Discovering new materials and new phenomena with evolutionary algorithms

NASA Astrophysics Data System (ADS)

Oganov, Artem

Thanks to powerful evolutionary algorithms, in particular the USPEX method, it is now possible to predict both the stable compounds and their crystal structures at arbitrary conditions, given just the set of chemical elements. Recent developments include major increases of efficiency and extensions to low-dimensional systems and molecular crystals (which allowed large structures to be handled easily, e.g. Mg(BH4)2 and H2O-H2) and new techniques called evolutionary metadynamics and Mendelevian search. Some of the results that I will discuss include: 1. Theoretical and experimental evidence for a new partially ionic phase of boron, γ-B and an insulating and optically transparent form of sodium. 2. Predicted stability of ``impossible'' chemical compounds that become stable under pressure - e.g. Na3Cl, Na2Cl, Na3Cl2, NaCl3, NaCl7, Mg3O2 and MgO2. 3. Novel surface phases (e.g. boron surface reconstructions). 4. Novel dielectric polymers, and novel permanent magnets confirmed by experiment and ready for applications. 5. Prediction of new ultrahard materials and computational proof that diamond is the hardest possible material.

Evolutionary dynamics of fluctuating populations with strong mutualism

NASA Astrophysics Data System (ADS)

Chotibut, Thiparat; Nelson, David

2013-03-01

Evolutionary game theory with finite interacting populations is receiving increased attention, including subtle phenomena associated with number fluctuations, i.e., ``genetic drift.'' Models of cooperation and competition often utilize a simplified Moran model, with a strictly fixed total population size. We explore a more general evolutionary model with independent fluctuations in the numbers of two distinct species, in a regime characterized by ``strong mutualism.'' The model has two absorbing states, each corresponding to fixation of one of the two species, and allows exploration of the interplay between growth, competition, and mutualism. When mutualism is favored, number fluctuations eventually drive the system away from a stable fixed point, characterized by cooperation, to one of the absorbing states. Well-mixed populations will thus be taken over by a single species in a finite time, despite the bias towards cooperation. We calculate both the fixation probability and the mean fixation time as a function of the initial conditions and carrying capacities in the strong mutualism regime, using the method of matched asymptotic expansions. Our results are compared to computer simulations.

Detection of timescales in evolving complex systems

PubMed Central

Darst, Richard K.; Granell, Clara; Arenas, Alex; Gómez, Sergio; Saramäki, Jari; Fortunato, Santo

2016-01-01

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system’s configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system. PMID:28004820

Data Structures in Natural Computing: Databases as Weak or Strong Anticipatory Systems

NASA Astrophysics Data System (ADS)

Rossiter, B. N.; Heather, M. A.

2004-08-01

Information systems anticipate the real world. Classical databases store, organise and search collections of data of that real world but only as weak anticipatory information systems. This is because of the reductionism and normalisation needed to map the structuralism of natural data on to idealised machines with von Neumann architectures consisting of fixed instructions. Category theory developed as a formalism to explore the theoretical concept of naturality shows that methods like sketches arising from graph theory as only non-natural models of naturality cannot capture real-world structures for strong anticipatory information systems. Databases need a schema of the natural world. Natural computing databases need the schema itself to be also natural. Natural computing methods including neural computers, evolutionary automata, molecular and nanocomputing and quantum computation have the potential to be strong. At present they are mainly at the stage of weak anticipatory systems.

Bottleneck Effect on Evolutionary Rate in the Nearly Neutral Mutation Model

PubMed Central

Araki, H.; Tachida, H.

1997-01-01

Variances of evolutionary rates among lineages in some proteins are larger than those expected from simple Poisson processes. This phenomenon is called overdispersion of the molecular clock. If population size N is constant, the overdispersion is observed only in a limited range of 2Nσ under the nearly neutral mutation model, where σ represents the standard deviation of selection coefficients of new mutants. In this paper, we investigated effects of changing population size on the evolutionary rate by computer simulations assuming the nearly neutral mutation model. The size was changed cyclically between two numbers, N(1) and N(2) (N(1) > N(2)), in the simulations. The overdispersion is observed if 2N(2)σ is less than two and the state of reduced size (bottleneck state) continues for more than ~0.1/u generations, where u is the mutation rate. The overdispersion results mainly because the average fitnesses of only a portion of populations go down when the population size is reduced and only in these populations subsequent advantageous substitutions occur after the population size becomes large. Since the fitness reduction after the bottleneck is stochastic, acceleration of the evolutionary rate does not necessarily occur uniformly among loci. From these results, we argue that the nearly neutral mutation model is a candidate mechanism to explain the overdispersed molecular clock. PMID:9335622

An Improved Evolutionary Programming with Voting and Elitist Dispersal Scheme

NASA Astrophysics Data System (ADS)

Maity, Sayan; Gunjan, Kumar; Das, Swagatam

Although initially conceived for evolving finite state machines, Evolutionary Programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we present an improved EP variant called Evolutionary Programming with Voting and Elitist Dispersal (EPVE). The scheme encompasses a voting process which not only gives importance to best solutions but also consider those solutions which are converging fast. By introducing Elitist Dispersal Scheme we maintain the elitism by keeping the potential solutions intact and other solutions are perturbed accordingly, so that those come out of the local minima. By applying these two techniques we can be able to explore those regions which have not been explored so far that may contain optima. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on Evolutionary Computation) 2005 test-suite for real parameter optimization reflects the superiority of the new scheme in terms of final accuracy, speed, and robustness.

DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood.

PubMed

Kim, Tane; Hao, Weilong

2014-09-27

The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired. DiscML performs maximum likelihood estimation for evolutionary rates of discrete characters on a provided phylogeny with the options that correct for unobservable data, rate variations, and unknown prior root probabilities from the empirical data. It gives users options to customize the instantaneous transition rate matrices, or to choose pre-determined matrices from models such as birth-and-death (BD), birth-death-and-innovation (BDI), equal rates (ER), symmetric (SYM), general time-reversible (GTR) and all rates different (ARD). Moreover, we show application examples of DiscML on gene family data and on intron presence/absence data. DiscML was developed as a unified R program for estimating evolutionary rates of discrete characters with no restriction on the number of character states, and with flexibility to use different transition models. DiscML is ideal for the analyses of binary (1s/0s) patterns, multi-gene families, and multistate discrete morphological characteristics.

Joint attention and language evolution

NASA Astrophysics Data System (ADS)

Kwisthout, Johan; Vogt, Paul; Haselager, Pim; Dijkstra, Ton

2008-06-01

This study investigates how more advanced joint attentional mechanisms, rather than only shared attention between two agents and an object, can be implemented and how they influence the results of language games played by these agents. We present computer simulations with language games showing that adding constructs that mimic the three stages of joint attention identified in children's early development (checking attention, following attention, and directing attention) substantially increase the performance of agents in these language games. In particular, the rates of improved performance for the individual attentional mechanisms have the same ordering as that of the emergence of these mechanisms in infants' development. These results suggest that language evolution and joint attentional mechanisms have developed in a co-evolutionary way, and that the evolutionary emergence of the individual attentional mechanisms is ordered just like their developmental emergence.

Ancient Wings: animating the evolution of butterfly wing patterns.

PubMed

Arbesman, Samuel; Enthoven, Leo; Monteiro, Antónia

2003-10-01

Character optimization methods can be used to reconstruct ancestral states at the internal nodes of phylogenetic trees. However, seldom are these ancestral states visualized collectively. Ancient Wings is a computer program that provides a novel method of visualizing the evolution of several morphological traits simultaneously. It allows users to visualize how the ventral hindwing pattern of 54 butterflies in the genus Bicyclus may have changed over time. By clicking on each of the nodes within the evolutionary tree, the user can see an animation of how wing size, eyespot size, and eyespot position relative the wing margin, have putatively evolved as a collective whole. Ancient Wings may be used as a pedagogical device as well as a research tool for hypothesis-generation in the fields of evolutionary, ecological, and developmental biology.

Computational architecture of the yeast regulatory network

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim

2005-12-01

The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.

Protein interface classification by evolutionary analysis

PubMed Central

2012-01-01

Background Distinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved. Results We present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts. Conclusions An evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org. PMID:23259833

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

A systematic mapping study of process mining

NASA Astrophysics Data System (ADS)

Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo

2018-05-01

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

Radiation-driven winds of hot stars. V - Wind models for central stars of planetary nebulae

NASA Technical Reports Server (NTRS)

Pauldrach, A.; Puls, J.; Kudritzki, R. P.; Mendez, R. H.; Heap, S. R.

1988-01-01

Wind models using the recent improvements of radiation driven wind theory by Pauldrach et al. (1986) and Pauldrach (1987) are presented for central stars of planetary nebulae. The models are computed along evolutionary tracks evolving with different stellar mass from the Asymptotic Giant Branch. We show that the calculated terminal wind velocities are in agreement with the observations and allow in principle an independent determination of stellar masses and radii. The computed mass-loss rates are in qualitative agreement with the occurrence of spectroscopic stellar wind features as a function of stellar effective temperature and gravity.

A novel approach to multiple sequence alignment using hadoop data grids.

PubMed

Sudha Sadasivam, G; Baktavatchalam, G

2010-01-01

Multiple alignment of protein sequences helps to determine evolutionary linkage and to predict molecular structures. The factors to be considered while aligning multiple sequences are speed and accuracy of alignment. Although dynamic programming algorithms produce accurate alignments, they are computation intensive. In this paper we propose a time efficient approach to sequence alignment that also produces quality alignment. The dynamic nature of the algorithm coupled with data and computational parallelism of hadoop data grids improves the accuracy and speed of sequence alignment. The principle of block splitting in hadoop coupled with its scalability facilitates alignment of very large sequences.

Three real-time architectures - A study using reward models

NASA Technical Reports Server (NTRS)

Sjogren, J. A.; Smith, R. M.

1990-01-01

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the evolutionary behavior of the computer system by a continuous-time Markov chain, and a reward rate is associated with each state. In reliability/availability models, upstates have reward rate 1, and down states have reward rate zero associated with them. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Steady-state expected reward rate and expected instantaneous reward rate are clearly useful measures which can be extracted from the Markov reward model. The diversity of areas where Markov reward models may be used is illustrated with a comparative study of three examples of interest to the fault tolerant computing community.

Photoionization modeling of the LWS fine-structure lines in IR bright galaxies

NASA Technical Reports Server (NTRS)

Satyapal, S.; Luhman, M. L.; Fischer, J.; Greenhouse, M. A.; Wolfire, M. G.

1997-01-01

The long wavelength spectrometer (LWS) fine structure line spectra from infrared luminous galaxies were modeled using stellar evolutionary synthesis models combined with photoionization and photodissociation region models. The calculations were carried out by using the computational code CLOUDY. Starburst and active galactic nuclei models are presented. The effects of dust in the ionized region are examined.

An Examination of Problem-Based Teaching and Learning in Population Genetics and Evolution Using EVOLVE, a Computer Simulation.

ERIC Educational Resources Information Center

Soderberg, Patti; Price, Frank

2003-01-01

Examines a lesson in which students are engaged in inquiry in evolutionary biology to develop better understanding of concepts and reasoning skills necessary to support knowledge claims about changes in the genetic structure of populations known as microevolution. Explains how a software simulation, EVOLVE, can be used to foster discussions about…

Software Engineering Guidebook

NASA Technical Reports Server (NTRS)

Connell, John; Wenneson, Greg

1993-01-01

The Software Engineering Guidebook describes SEPG (Software Engineering Process Group) supported processes and techniques for engineering quality software in NASA environments. Three process models are supported: structured, object-oriented, and evolutionary rapid-prototyping. The guidebook covers software life-cycles, engineering, assurance, and configuration management. The guidebook is written for managers and engineers who manage, develop, enhance, and/or maintain software under the Computer Software Services Contract.

Data collection and storage in long-term ecological and evolutionary studies: The Mongoose 2000 system

PubMed Central

Griffiths, David J.; Mwanguhya, Francis; Businge, Robert; Griffiths, Amber G. F.; Kyabulima, Solomon; Mwesige, Kenneth; Sanderson, Jennifer L.; Thompson, Faye J.; Vitikainen, Emma I. K.; Cant, Michael A.

2018-01-01

Studying ecological and evolutionary processes in the natural world often requires research projects to follow multiple individuals in the wild over many years. These projects have provided significant advances but may also be hampered by needing to accurately and efficiently collect and store multiple streams of the data from multiple individuals concurrently. The increase in the availability and sophistication of portable computers (smartphones and tablets) and the applications that run on them has the potential to address many of these data collection and storage issues. In this paper we describe the challenges faced by one such long-term, individual-based research project: the Banded Mongoose Research Project in Uganda. We describe a system we have developed called Mongoose 2000 that utilises the potential of apps and portable computers to meet these challenges. We discuss the benefits and limitations of employing such a system in a long-term research project. The app and source code for the Mongoose 2000 system are freely available and we detail how it might be used to aid data collection and storage in other long-term individual-based projects. PMID:29315317

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

PubMed

Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

2014-03-01

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

A Distance Measure for Genome Phylogenetic Analysis

NASA Astrophysics Data System (ADS)

Cao, Minh Duc; Allison, Lloyd; Dix, Trevor

Phylogenetic analyses of species based on single genes or parts of the genomes are often inconsistent because of factors such as variable rates of evolution and horizontal gene transfer. The availability of more and more sequenced genomes allows phylogeny construction from complete genomes that is less sensitive to such inconsistency. For such long sequences, construction methods like maximum parsimony and maximum likelihood are often not possible due to their intensive computational requirement. Another class of tree construction methods, namely distance-based methods, require a measure of distances between any two genomes. Some measures such as evolutionary edit distance of gene order and gene content are computational expensive or do not perform well when the gene content of the organisms are similar. This study presents an information theoretic measure of genetic distances between genomes based on the biological compression algorithm expert model. We demonstrate that our distance measure can be applied to reconstruct the consensus phylogenetic tree of a number of Plasmodium parasites from their genomes, the statistical bias of which would mislead conventional analysis methods. Our approach is also used to successfully construct a plausible evolutionary tree for the γ-Proteobacteria group whose genomes are known to contain many horizontally transferred genes.

Three-dimensional deformable-model-based localization and recognition of road vehicles.

PubMed

Zhang, Zhaoxiang; Tan, Tieniu; Huang, Kaiqi; Wang, Yunhong

2012-01-01

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.

Using evolutionary computation to optimize an SVM used in detecting buried objects in FLIR imagery

NASA Astrophysics Data System (ADS)

Paino, Alex; Popescu, Mihail; Keller, James M.; Stone, Kevin

2013-06-01

In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an Evolutionary Computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Hidden relationships between metalloproteins unveiled by structural comparison of their metal sites

NASA Astrophysics Data System (ADS)

Valasatava, Yana; Andreini, Claudia; Rosato, Antonio

2015-03-01

Metalloproteins account for a substantial fraction of all proteins. They incorporate metal atoms, which are required for their structure and/or function. Here we describe a new computational protocol to systematically compare and classify metal-binding sites on the basis of their structural similarity. These sites are extracted from the MetalPDB database of minimal functional sites (MFSs) in metal-binding biological macromolecules. Structural similarity is measured by the scoring function of the available MetalS2 program. Hierarchical clustering was used to organize MFSs into clusters, for each of which a representative MFS was identified. The comparison of all representative MFSs provided a thorough structure-based classification of the sites analyzed. As examples, the application of the proposed computational protocol to all heme-binding proteins and zinc-binding proteins of known structure highlighted the existence of structural subtypes, validated known evolutionary links and shed new light on the occurrence of similar sites in systems at different evolutionary distances. The present approach thus makes available an innovative viewpoint on metalloproteins, where the functionally crucial metal sites effectively lead the discovery of structural and functional relationships in a largely protein-independent manner.

Evolutionary Action Score of TP53 Identifies High-Risk Mutations Associated with Decreased Survival and Increased Distant Metastases in Head and Neck Cancer.

PubMed

Neskey, David M; Osman, Abdullah A; Ow, Thomas J; Katsonis, Panagiotis; McDonald, Thomas; Hicks, Stephanie C; Hsu, Teng-Kuei; Pickering, Curtis R; Ward, Alexandra; Patel, Ameeta; Yordy, John S; Skinner, Heath D; Giri, Uma; Sano, Daisuke; Story, Michael D; Beadle, Beth M; El-Naggar, Adel K; Kies, Merrill S; William, William N; Caulin, Carlos; Frederick, Mitchell; Kimmel, Marek; Myers, Jeffrey N; Lichtarge, Olivier

2015-04-01

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma, with mutations occurring in over two-thirds of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed evolutionary action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high-risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high-risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high-risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations that confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations. ©2015 American Association for Cancer Research.

Evolutionary Action score of TP53 (EAp53) identifies high risk mutations associated with decreased survival and increased distant metastases in head and neck cancer

PubMed Central

Neskey, David M.; Osman, Abdullah A.; Ow, Thomas J.; Katsonis, Panagiotis; McDonald, Thomas; Hicks, Stephanie C.; Hsu, Teng-Kuei; Pickering, Curtis R.; Ward, Alexandra; Patel, Ameeta; Yordy, John S.; Skinner, Heath D.; Giri, Uma; Sano, Daisuke; Story, Michael D.; Beadle, Beth M.; El-Naggar, Adel K.; Kies, Merrill S.; William, William N.; Caulin, Carlos; Frederick, Mitchell; Kimmel, Marek; Myers, Jeffrey N.; Lichtarge, Olivier

2015-01-01

TP53 is the most frequently altered gene in head and neck squamous cell carcinoma (HNSCC) with mutations occurring in over two third of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed Evolutionary Action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations which confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations. PMID:25634208

Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles

NASA Technical Reports Server (NTRS)

Tessler, Alexander

2007-01-01

Two finite element based computational methods, Smoothing Element Analysis (SEA) and the inverse Finite Element Method (iFEM), are reviewed, and examples of their use for structural health monitoring are discussed. Due to their versatility, robustness, and computational efficiency, the methods are well suited for real-time structural health monitoring of future space vehicles, large space structures, and habitats. The methods may be effectively employed to enable real-time processing of sensing information, specifically for identifying three-dimensional deformed structural shapes as well as the internal loads. In addition, they may be used in conjunction with evolutionary algorithms to design optimally distributed sensors. These computational tools have demonstrated substantial promise for utilization in future Structural Health Management (SHM) systems.

Computational intelligence approaches for pattern discovery in biological systems.

PubMed

Fogel, Gary B

2008-07-01

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Computational Aerothermodynamic Design Issues for Hypersonic Vehicles

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.; Weilmuenster, K. James; Hamilton, H. Harris, II; Olynick, David R.; Venkatapathy, Ethiraj

1997-01-01

A brief review of the evolutionary progress in computational aerothermodynamics is presented. The current status of computational aerothermodynamics is then discussed, with emphasis on its capabilities and limitations for contributions to the design process of hypersonic vehicles. Some topics to be highlighted include: (1) aerodynamic coefficient predictions with emphasis on high temperature gas effects; (2) surface heating and temperature predictions for thermal protection system (TPS) design in a high temperature, thermochemical nonequilibrium environment; (3) methods for extracting and extending computational fluid dynamic (CFD) solutions for efficient utilization by all members of a multidisciplinary design team; (4) physical models; (5) validation process and error estimation; and (6) gridding and solution generation strategies. Recent experiences in the design of X-33 will be featured. Computational aerothermodynamic contributions to Mars Pathfinder, METEOR, and Stardust (Comet Sample return) will also provide context for this discussion. Some of the barriers that currently limit computational aerothermodynamics to a predominantly reactive mode in the design process will also be discussed, with the goal of providing focus for future research.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

PubMed

Lee, Kang-Hoon; Shin, Kyung-Seop; Lim, Debora; Kim, Woo-Chan; Chung, Byung Chang; Han, Gyu-Bum; Roh, Jeongkyu; Cho, Dong-Ho; Cho, Kiho

2015-07-01

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes. Copyright © 2015 Elsevier Inc. All rights reserved.

Computational Aerothermodynamic Design Issues for Hypersonic Vehicles

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.; Weilmuenster, K. James; Hamilton, H. Harris, II; Olynick, David R.; Venkatapathy, Ethiraj

2005-01-01

A brief review of the evolutionary progress in computational aerothermodynamics is presented. The current status of computational aerothermodynamics is then discussed, with emphasis on its capabilities and limitations for contributions to the design process of hypersonic vehicles. Some topics to be highlighted include: (1) aerodynamic coefficient predictions with emphasis on high temperature gas effects; (2) surface heating and temperature predictions for thermal protection system (TPS) design in a high temperature, thermochemical nonequilibrium environment; (3) methods for extracting and extending computational fluid dynamic (CFD) solutions for efficient utilization by all members of a multidisciplinary design team; (4) physical models; (5) validation process and error estimation; and (6) gridding and solution generation strategies. Recent experiences in the design of X-33 will be featured. Computational aerothermodynamic contributions to Mars Path finder, METEOR, and Stardust (Comet Sample return) will also provide context for this discussion. Some of the barriers that currently limit computational aerothermodynamics to a predominantly reactive mode in the design process will also be discussed, with the goal of providing focus for future research.

Computational Aerothermodynamic Design Issues for Hypersonic Vehicles

NASA Technical Reports Server (NTRS)

Olynick, David R.; Venkatapathy, Ethiraj

2004-01-01

A brief review of the evolutionary progress in computational aerothermodynamics is presented. The current status of computational aerothermodynamics is then discussed, with emphasis on its capabilities and limitations for contributions to the design process of hypersonic vehicles. Some topics to be highlighted include: (1) aerodynamic coefficient predictions with emphasis on high temperature gas effects; (2) surface heating and temperature predictions for thermal protection system (TPS) design in a high temperature, thermochemical nonequilibrium environment; (3) methods for extracting and extending computational fluid dynamic (CFD) solutions for efficient utilization by all members of a multidisciplinary design team; (4) physical models; (5) validation process and error estimation; and (6) gridding and solution generation strategies. Recent experiences in the design of X-33 will be featured. Computational aerothermodynamic contributions to Mars Pathfinder, METEOR, and Stardust (Comet Sample return) will also provide context for this discussion. Some of the barriers that currently limit computational aerothermodynamics to a predominantly reactive mode in the design process will also be discussed, with the goal of providing focus for future research.

Epigenetic game theory and its application in plants. Comment on: ;Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition; by Qian Wang et al.

NASA Astrophysics Data System (ADS)

Zhang, Yuan-Ming; Zhang, Yinghao; Guo, Mingyue

2017-03-01

Wang's et al. article [1] is the first to integrate game theory (especially evolutionary game theory) with epigenetic modification of zygotic genomes. They described and assessed a modeling framework based on evolutionary game theory to quantify, how sperms and oocytes interact through epigenetic processes, to determine embryo development. They also studied the internal mechanisms for normal embryo development: 1) evolutionary interactions between DNA methylation of the paternal and maternal genomes, and 2) the application of game theory to formulate and quantify how different genes compete or cooperate to regulate embryogenesis through methylation. Although it is not very comprehensive and profound regarding game theory modeling, this article bridges the gap between evolutionary game theory and the epigenetic control of embryo development by powerful ordinary differential equations (ODEs). The epiGame framework includes four aspects: 1) characterizing how epigenetic game theory works by the strategy matrix, in which the pattern and relative magnitude of the methylation effects on embryogenesis, are described by the cooperation and competition mechanisms, 2) quantifying the game that the direction and degree of P-M interactions over embryo development can be explained by the sign and magnitude of interaction parameters in model (2), 3) modeling epigenetic interactions within the morula, especially for two coupled nonlinear ODEs, with explicit functions in model (4), which provide a good fit to the observed data for the two sexes (adjusted R2 = 0.956), and 4) revealing multifactorial interactions in embryogenesis from the coupled ODEs in model (2) to triplet ODEs in model (6). Clearly, this article extends game theory from evolutionary game theory to epigenetic game theory.

Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability.

PubMed

Park, Jong C; Kim, Tak-eun; Park, Jinah

2008-04-11

Much effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of particular changes along the time axis on a larger scale. We present a method for tapping into such an evolutionary aspect of GO, by making it possible to keep track of important temporal changes to any of the terms and relations of GO and by consequently making it possible to recognize associated trends. We have developed visualization methods for viewing the changes between two different versions of GO by constructing a colour-coded layered graph. The graph shows both versions of GO with highlights to those GO terms that are added, removed and modified between the two versions. Focusing on a specific GO term or terms of interest over a period, we demonstrate the utility of our system that can be used to make useful hypotheses about the cause of the evolution and to provide new insights into more complex changes. GO undergoes fast evolutionary changes. A snapshot of GO, as presented by each version of GO alone, overlooks such evolutionary aspects, and consequently limits the utilities of GO. The method that highlights the differences of consecutive versions or two different versions of an evolving ontology with colour-coding enhances the utility of GO for users as well as for developers. To the best of our knowledge, this is the first proposal to visualize the evolutionary aspect of GO.

Evolution of attention mechanisms for early visual processing

NASA Astrophysics Data System (ADS)

Müller, Thomas; Knoll, Alois

2011-03-01

Early visual processing as a method to speed up computations on visual input data has long been discussed in the computer vision community. The general target of a such approaches is to filter nonrelevant information from the costly higher-level visual processing algorithms. By insertion of this additional filter layer the overall approach can be speeded up without actually changing the visual processing methodology. Being inspired by the layered architecture of the human visual processing apparatus, several approaches for early visual processing have been recently proposed. Most promising in this field is the extraction of a saliency map to determine regions of current attention in the visual field. Such saliency can be computed in a bottom-up manner, i.e. the theory claims that static regions of attention emerge from a certain color footprint, and dynamic regions of attention emerge from connected blobs of textures moving in a uniform way in the visual field. Top-down saliency effects are either unconscious through inherent mechanisms like inhibition-of-return, i.e. within a period of time the attention level paid to a certain region automatically decreases if the properties of that region do not change, or volitional through cognitive feedback, e.g. if an object moves consistently in the visual field. These bottom-up and top-down saliency effects have been implemented and evaluated in a previous computer vision system for the project JAST. In this paper an extension applying evolutionary processes is proposed. The prior vision system utilized multiple threads to analyze the regions of attention delivered from the early processing mechanism. Here, in addition, multiple saliency units are used to produce these regions of attention. All of these saliency units have different parameter-sets. The idea is to let the population of saliency units create regions of attention, then evaluate the results with cognitive feedback and finally apply the genetic mechanism: mutation and cloning of the best performers and extinction of the worst performers considering computation of regions of attention. A fitness function can be derived by evaluating, whether relevant objects are found in the regions created. It can be seen from various experiments, that the approach significantly speeds up visual processing, especially regarding robust ealtime object recognition, compared to an approach not using saliency based preprocessing. Furthermore, the evolutionary algorithm improves the overall performance of the preprocessing system in terms of quality, as the system automatically and autonomously tunes the saliency parameters. The computational overhead produced by periodical clone/delete/mutation operations can be handled well within the realtime constraints of the experimental computer vision system. Nevertheless, limitations apply whenever the visual field does not contain any significant saliency information for some time, but the population still tries to tune the parameters - overfitting avoids generalization in this case and the evolutionary process may be reset by manual intervention.

EHW Approach to Temperature Compensation of Electronics

NASA Technical Reports Server (NTRS)

Stoica, Adrian

2004-01-01

Efforts are under way to apply the concept of evolvable hardware (EHW) to compensate for variations, with temperature, in the operational characteristics of electronic circuits. To maintain the required functionality of a given circuit at a temperature above or below the nominal operating temperature for which the circuit was originally designed, a new circuit would be evolved; moreover, to obtain the required functionality over a very wide temperature range, there would be evolved a number of circuits, each of which would satisfy the performance requirements over a small part of the total temperature range. The basic concepts and some specific implementations of EHW were described in a number of previous NASA Tech Briefs articles, namely, "Reconfigurable Arrays of Transistors for Evolvable Hardware" (NPO-20078), Vol. 25, No. 2 (February 2001), page 36; Evolutionary Automated Synthesis of Electronic Circuits (NPO- 20535), Vol. 26, No. 7 (July 2002), page 37; "Designing Reconfigurable Antennas Through Hardware Evolution" (NPO-20666), Vol. 26, No. 7 (July 2002), page 38; "Morphing in Evolutionary Synthesis of Electronic Circuits" (NPO-20837), Vol. 26, No. 8 (August 2002), page 31; "Mixtrinsic Evolutionary Synthesis of Electronic Circuits" (NPO-20773) Vol. 26, No. 8 (August 2002), page 32; and "Synthesis of Fuzzy-Logic Circuits in Evolvable Hardware" (NPO-21095) Vol. 26, No. 11 (November 2002), page 38. To recapitulate from the cited prior articles: EHW is characterized as evolutionary in a quasi-genetic sense. The essence of EHW is to construct and test a sequence of populations of circuits that function as incrementally better solutions of a given design problem through the selective, repetitive connection and/or disconnection of capacitors, transistors, amplifiers, inverters, and/or other circuit building blocks. The connection and disconnection can be effected by use of field-programmable transistor arrays (FPTAs). The evolution is guided by a search-andoptimization algorithm (in particular, a genetic algorithm) that operates in the space of possible circuits to find a circuit that exhibits an acceptably close approximation of the desired functionality. The evolved circuits can be tested by mathematical modeling (that is, computational simulation) only, tested in real hardware, or tested in combinations of computational simulation and real hardware.

Assessing the evolutionary rate of positional orthologous genes in prokaryotes using synteny data

PubMed Central

Lemoine, Frédéric; Lespinet, Olivier; Labedan, Bernard

2007-01-01

Background Comparison of completely sequenced microbial genomes has revealed how fluid these genomes are. Detecting synteny blocks requires reliable methods to determining the orthologs among the whole set of homologs detected by exhaustive comparisons between each pair of completely sequenced genomes. This is a complex and difficult problem in the field of comparative genomics but will help to better understand the way prokaryotic genomes are evolving. Results We have developed a suite of programs that automate three essential steps to study conservation of gene order, and validated them with a set of 107 bacteria and archaea that cover the majority of the prokaryotic taxonomic space. We identified the whole set of shared homologs between two or more species and computed the evolutionary distance separating each pair of homologs. We applied two strategies to extract from the set of homologs a collection of valid orthologs shared by at least two genomes. The first computes the Reciprocal Smallest Distance (RSD) using the PAM distances separating pairs of homologs. The second method groups homologs in families and reconstructs each family's evolutionary tree, distinguishing bona fide orthologs as well as paralogs created after the last speciation event. Although the phylogenetic tree method often succeeds where RSD fails, the reverse could occasionally be true. Accordingly, we used the data obtained with either methods or their intersection to number the orthologs that are adjacent in for each pair of genomes, the Positional Orthologous Genes (POGs), and to further study their properties. Once all these synteny blocks have been detected, we showed that POGs are subject to more evolutionary constraints than orthologs outside synteny groups, whichever the taxonomic distance separating the compared organisms. Conclusion The suite of programs described in this paper allows a reliable detection of orthologs and is useful for evaluating gene order conservation in prokaryotes whichever their taxonomic distance. Thus, our approach will make easy the rapid identification of POGS in the next few years as we are expecting to be inundated with thousands of completely sequenced microbial genomes. PMID:18047665

Interplay between cooperation-enhancing mechanisms in evolutionary games with tag-mediated interactions

NASA Astrophysics Data System (ADS)

Hadzibeganovic, Tarik; Stauffer, Dietrich; Han, Xiao-Pu

2018-04-01

Cooperation is fundamental for the long-term survival of biological, social, and technological networks. Previously, mechanisms for the enhancement of cooperation, such as network reciprocity, have largely been studied in isolation and with often inconclusive findings. Here, we present an evolutionary, multiagent-based, and spatially explicit computer model to specifically address the interactive interplay between such mechanisms. We systematically investigate the effects of phenotypic diversity, network structure, and rewards on cooperative behavior emerging in a population of reproducing artificial decision makers playing tag-mediated evolutionary games. Cooperative interactions are rewarded such that both the benefits of recipients and costs of donators are affected by the reward size. The reward size is determined by the number of cooperative acts occurring within a given reward time frame. Our computational experiments reveal that small reward frames promote unconditional cooperation in populations with both low and high diversity, whereas large reward frames lead to cycles of conditional and unconditional strategies at high but not at low diversity. Moreover, an interaction between rewards and spatial structure shows that relative to small reward frames, there is a strong difference between the frequency of conditional cooperators populating rewired versus non-rewired networks when the reward frame is large. Notably, in a less diverse population, the total number of defections is comparable across different network topologies, whereas in more diverse environments defections become more frequent in a regularly structured than in a rewired, small-world network of contacts. Acknowledging the importance of such interaction effects in social dilemmas will have inevitable consequences for the future design of cooperation-enhancing protocols in large-scale, distributed, and decentralized systems such as peer-to-peer networks.

From cacti to carnivores: Improved phylotranscriptomic sampling and hierarchical homology inference provide further insight into the evolution of Caryophyllales.

PubMed

Walker, Joseph F; Yang, Ya; Feng, Tao; Timoneda, Alfonso; Mikenas, Jessica; Hutchison, Vera; Edwards, Caroline; Wang, Ning; Ahluwalia, Sonia; Olivieri, Julia; Walker-Hale, Nathanael; Majure, Lucas C; Puente, Raúl; Kadereit, Gudrun; Lauterbach, Maximilian; Eggli, Urs; Flores-Olvera, Hilda; Ochoterena, Helga; Brockington, Samuel F; Moore, Michael J; Smith, Stephen A

2018-03-01

The Caryophyllales contain ~12,500 species and are known for their cosmopolitan distribution, convergence of trait evolution, and extreme adaptations. Some relationships within the Caryophyllales, like those of many large plant clades, remain unclear, and phylogenetic studies often recover alternative hypotheses. We explore the utility of broad and dense transcriptome sampling across the order for resolving evolutionary relationships in Caryophyllales. We generated 84 transcriptomes and combined these with 224 publicly available transcriptomes to perform a phylogenomic analysis of Caryophyllales. To overcome the computational challenge of ortholog detection in such a large data set, we developed an approach for clustering gene families that allowed us to analyze >300 transcriptomes and genomes. We then inferred the species relationships using multiple methods and performed gene-tree conflict analyses. Our phylogenetic analyses resolved many clades with strong support, but also showed significant gene-tree discordance. This discordance is not only a common feature of phylogenomic studies, but also represents an opportunity to understand processes that have structured phylogenies. We also found taxon sampling influences species-tree inference, highlighting the importance of more focused studies with additional taxon sampling. Transcriptomes are useful both for species-tree inference and for uncovering evolutionary complexity within lineages. Through analyses of gene-tree conflict and multiple methods of species-tree inference, we demonstrate that phylogenomic data can provide unparalleled insight into the evolutionary history of Caryophyllales. We also discuss a method for overcoming computational challenges associated with homolog clustering in large data sets. © 2018 The Authors. American Journal of Botany is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

NASA Astrophysics Data System (ADS)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

Computational characterization of HPGe detectors usable for a wide variety of source geometries by using Monte Carlo simulation and a multi-objective evolutionary algorithm

NASA Astrophysics Data System (ADS)

Guerra, J. G.; Rubiano, J. G.; Winter, G.; Guerra, A. G.; Alonso, H.; Arnedo, M. A.; Tejera, A.; Martel, P.; Bolivar, J. P.

2017-06-01

In this work, we have developed a computational methodology for characterizing HPGe detectors by implementing in parallel a multi-objective evolutionary algorithm, together with a Monte Carlo simulation code. The evolutionary algorithm is used for searching the geometrical parameters of a model of detector by minimizing the differences between the efficiencies calculated by Monte Carlo simulation and two reference sets of Full Energy Peak Efficiencies (FEPEs) corresponding to two given sample geometries, a beaker of small diameter laid over the detector window and a beaker of large capacity which wrap the detector. This methodology is a generalization of a previously published work, which was limited to beakers placed over the window of the detector with a diameter equal or smaller than the crystal diameter, so that the crystal mount cap (which surround the lateral surface of the crystal), was not considered in the detector model. The generalization has been accomplished not only by including such a mount cap in the model, but also using multi-objective optimization instead of mono-objective, with the aim of building a model sufficiently accurate for a wider variety of beakers commonly used for the measurement of environmental samples by gamma spectrometry, like for instance, Marinellis, Petris, or any other beaker with a diameter larger than the crystal diameter, for which part of the detected radiation have to pass through the mount cap. The proposed methodology has been applied to an HPGe XtRa detector, providing a model of detector which has been successfully verificated for different source-detector geometries and materials and experimentally validated using CRMs.

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution.

PubMed

Chakrabarti, Shaon; Michor, Franziska

2017-07-15

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR . ©2017 American Association for Cancer Research.

The Probability of a Gene Tree Topology within a Phylogenetic Network with Applications to Hybridization Detection

PubMed Central

Yu, Yun; Degnan, James H.; Nakhleh, Luay

2012-01-01

Gene tree topologies have proven a powerful data source for various tasks, including species tree inference and species delimitation. Consequently, methods for computing probabilities of gene trees within species trees have been developed and widely used in probabilistic inference frameworks. All these methods assume an underlying multispecies coalescent model. However, when reticulate evolutionary events such as hybridization occur, these methods are inadequate, as they do not account for such events. Methods that account for both hybridization and deep coalescence in computing the probability of a gene tree topology currently exist for very limited cases. However, no such methods exist for general cases, owing primarily to the fact that it is currently unknown how to compute the probability of a gene tree topology within the branches of a phylogenetic network. Here we present a novel method for computing the probability of gene tree topologies on phylogenetic networks and demonstrate its application to the inference of hybridization in the presence of incomplete lineage sorting. We reanalyze a Saccharomyces species data set for which multiple analyses had converged on a species tree candidate. Using our method, though, we show that an evolutionary hypothesis involving hybridization in this group has better support than one of strict divergence. A similar reanalysis on a group of three Drosophila species shows that the data is consistent with hybridization. Further, using extensive simulation studies, we demonstrate the power of gene tree topologies at obtaining accurate estimates of branch lengths and hybridization probabilities of a given phylogenetic network. Finally, we discuss identifiability issues with detecting hybridization, particularly in cases that involve extinction or incomplete sampling of taxa. PMID:22536161

Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface

PubMed Central

2012-01-01

Background Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface. Methods This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima. Results and conclusions The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework. PMID:22759582

Ensemble modeling of stochastic unsteady open-channel flow in terms of its time-space evolutionary probability distribution - Part 2: numerical application

NASA Astrophysics Data System (ADS)

Dib, Alain; Kavvas, M. Levent

2018-03-01

The characteristic form of the Saint-Venant equations is solved in a stochastic setting by using a newly proposed Fokker-Planck Equation (FPE) methodology. This methodology computes the ensemble behavior and variability of the unsteady flow in open channels by directly solving for the flow variables' time-space evolutionary probability distribution. The new methodology is tested on a stochastic unsteady open-channel flow problem, with an uncertainty arising from the channel's roughness coefficient. The computed statistical descriptions of the flow variables are compared to the results obtained through Monte Carlo (MC) simulations in order to evaluate the performance of the FPE methodology. The comparisons show that the proposed methodology can adequately predict the results of the considered stochastic flow problem, including the ensemble averages, variances, and probability density functions in time and space. Unlike the large number of simulations performed by the MC approach, only one simulation is required by the FPE methodology. Moreover, the total computational time of the FPE methodology is smaller than that of the MC approach, which could prove to be a particularly crucial advantage in systems with a large number of uncertain parameters. As such, the results obtained in this study indicate that the proposed FPE methodology is a powerful and time-efficient approach for predicting the ensemble average and variance behavior, in both space and time, for an open-channel flow process under an uncertain roughness coefficient.

Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature

NASA Astrophysics Data System (ADS)

Ma, Zhanshan (Sam)

In evolutionary computing (EC), population size is one of the critical parameters that a researcher has to deal with. Hence, it was no surprise that the pioneers of EC, such as De Jong (1975) and Holland (1975), had already studied the population sizing from the very beginning of EC. What is perhaps surprising is that more than three decades later, we still largely depend on the experience or ad-hoc trial-and-error approach to set the population size. For example, in a recent monograph, Eiben and Smith (2003) indicated: "In almost all EC applications, the population size is constant and does not change during the evolutionary search." Despite enormous research on this issue in recent years, we still lack a well accepted theory for population sizing. In this paper, I propose to develop a population dynamics theory forEC with the inspiration from the population dynamics theory of biological populations in nature. Essentially, the EC population is considered as a dynamic system over time (generations) and space (search space or fitness landscape), similar to the spatial and temporal dynamics of biological populations in nature. With this conceptual mapping, I propose to 'transplant' the biological population dynamics theory to EC via three steps: (i) experimentally test the feasibility—whether or not emulating natural population dynamics improves the EC performance; (ii) comparatively study the underlying mechanisms—why there are improvements, primarily via statistical modeling analysis; (iii) conduct theoretical analysis with theoretical models such as percolation theory and extended evolutionary game theory that are generally applicable to both EC and natural populations. This article is a summary of a series of studies we have performed to achieve the general goal [27][30]-[32]. In the following, I start with an extremely brief introduction on the theory and models of natural population dynamics (Sections 1 & 2). In Sections 4 to 6, I briefly discuss three categories of population dynamics models: deterministic modeling with Logistic chaos map as an example, stochastic modeling with spatial distribution patterns as an example, as well as survival analysis and extended evolutionary game theory (EEGT) modeling. Sample experiment results with Genetic algorithms (GA) are presented to demonstrate the applications of these models. The proposed EC population dynamics approach also makes survival selection largely unnecessary or much simplified since the individuals are naturally selected (controlled) by the mathematical models for EC population dynamics.

Aggregation Pheromone System: A Real-parameter Optimization Algorithm using Aggregation Pheromones as the Base Metaphor

NASA Astrophysics Data System (ADS)

Tsutsui, Shigeyosi

This paper proposes an aggregation pheromone system (APS) for solving real-parameter optimization problems using the collective behavior of individuals which communicate using aggregation pheromones. APS was tested on several test functions used in evolutionary computation. The results showed APS could solve real-parameter optimization problems fairly well. The sensitivity analysis of control parameters of APS is also studied.

Thioredoxin and evolution

NASA Technical Reports Server (NTRS)

Buchanan, B. B.

1991-01-01

Comparisons of primary structure have revealed significant homology between the m type thioredoxins of chloroplasts and the thioredoxins from a variety of bacteria. Chloroplast thioredoxin f, by comparison, remains an enigma: certain residues are invariant with those of the other thioredoxins, but a phylogenetic relationship to bacterial or m thioredoxins seems distant. Knowledge of the evolutionary history of thioredoxin f is, nevertheless, of interest because of its role in photosynthesis. Therefore, we have attempted to gain information on the evolutionary history of chloroplast thioredoxin f, as well as m. Our goal was first to establish the utility of thioredoxin as a phylogenetic marker, and, if found suitable, to deduce the evolutionary histories of the chloroplast thioredoxins. To this end, we have constructed phylogenetic (minimal replacement) trees using computer analysis. The results show that the thioredoxins of bacteria and animals fall into distinct phylogenetic groups - the bacterial group resembling that derived from earlier 16s RNA analysis and the animal group showing a cluster consistent with known relationships. The chloroplast thioredoxins show a novel type of phylogenetic arrangement: one m type aligns with its counterpart of eukaryotic algae, cyanobacteria and other bacteria, whereas the second type (f type) tracks with animal thioredoxin. The results give new insight into the evolution of photosynthesis.

Expert-guided evolutionary algorithm for layout design of complex space stations

NASA Astrophysics Data System (ADS)

Qian, Zhiqin; Bi, Zhuming; Cao, Qun; Ju, Weiguo; Teng, Hongfei; Zheng, Yang; Zheng, Siyu

2017-08-01

The layout of a space station should be designed in such a way that different equipment and instruments are placed for the station as a whole to achieve the best overall performance. The station layout design is a typical nondeterministic polynomial problem. In particular, how to manage the design complexity to achieve an acceptable solution within a reasonable timeframe poses a great challenge. In this article, a new evolutionary algorithm has been proposed to meet such a challenge. It is called as the expert-guided evolutionary algorithm with a tree-like structure decomposition (EGEA-TSD). Two innovations in EGEA-TSD are (i) to deal with the design complexity, the entire design space is divided into subspaces with a tree-like structure; it reduces the computation and facilitates experts' involvement in the solving process. (ii) A human-intervention interface is developed to allow experts' involvement in avoiding local optimums and accelerating convergence. To validate the proposed algorithm, the layout design of one-space station is formulated as a multi-disciplinary design problem, the developed algorithm is programmed and executed, and the result is compared with those from other two algorithms; it has illustrated the superior performance of the proposed EGEA-TSD.

From head to tail: new models and approaches in primate functional anatomy and biomechanics.

PubMed

Organ, Jason M; Deleon, Valerie B; Wang, Qian; Smith, Timothy D

2010-04-01

This special issue of The Anatomical Record (AR) is based on interest generated by a symposium at the 2008 annual meeting of the American Association of Anatomists (AAA) at Experimental Biology, entitled "An Evolutionary Perspective on Human Anatomy." The development of this volume in turn provided impetus for a Biological Anthropology Mini-Meeting, organized by members of the AAA for the 2010 Experimental Biology meeting in Anaheim, California. The research presented in these pages reflects the themes of these symposia and provides a snapshot of the current state of primate functional anatomy and biomechanics research. The 17 articles in this special issue utilize new models and/or approaches to study long-standing questions about the evolution of our closest relatives, including soft-tissue dissection and microanatomical techniques, experimental approaches to morphology, kinematic and kinetic biomechanics, high-resolution computed tomography, and Finite Element Analysis (FEA). This volume continues a close historical association between the disciplines of anatomy and biological anthropology: anatomists benefit from an understanding of the evolutionary history of our modern form, and biological anthropologists rely on anatomical principles to make informed evolutionary inferences about our closest relatives. (c) 2010 Wiley-Liss, Inc.

Quantification of complex modular architecture in plants.

PubMed

Reeb, Catherine; Kaandorp, Jaap; Jansson, Fredrik; Puillandre, Nicolas; Dubuisson, Jean-Yves; Cornette, Raphaël; Jabbour, Florian; Coudert, Yoan; Patiño, Jairo; Flot, Jean-François; Vanderpoorten, Alain

2018-04-01

Morphometrics, the assignment of quantities to biological shapes, is a powerful tool to address taxonomic, evolutionary, functional and developmental questions. We propose a novel method for shape quantification of complex modular architecture in thalloid plants, whose extremely reduced morphologies, combined with the lack of a formal framework for thallus description, have long rendered taxonomic and evolutionary studies extremely challenging. Using graph theory, thalli are described as hierarchical series of nodes and edges, allowing for accurate, homologous and repeatable measurements of widths, lengths and angles. The computer program MorphoSnake was developed to extract the skeleton and contours of a thallus and automatically acquire, at each level of organization, width, length, angle and sinuosity measurements. Through the quantification of leaf architecture in Hymenophyllum ferns (Polypodiopsida) and a fully worked example of integrative taxonomy in the taxonomically challenging thalloid liverwort genus Riccardia, we show that MorphoSnake is applicable to all ramified plants. This new possibility of acquiring large numbers of quantitative traits in plants with complex modular architectures opens new perspectives of applications, from the development of rapid species identification tools to evolutionary analyses of adaptive plasticity. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

Sixty-Five Million Years of Change in Temperature and Topography Explain Evolutionary History in Eastern North American Plethodontid Salamanders.

PubMed

Barnes, Richard; Clark, Adam Thomas

2017-07-01

For many taxa and systems, species richness peaks at midelevations. One potential explanation for this pattern is that large-scale changes in climate and geography have, over evolutionary time, selected for traits that are favored under conditions found in contemporary midelevation regions. To test this hypothesis, we use records of historical temperature and topographic changes over the past 65 Myr to construct a general simulation model of plethodontid salamander evolution in eastern North America. We then explore possible mechanisms constraining species to midelevation bands by using the model to predict plethodontid evolutionary history and contemporary geographic distributions. Our results show that models that incorporate both temperature and topographic changes are better able to predict these patterns, suggesting that both processes may have played an important role in driving plethodontid evolution in the region. Additionally, our model (whose annotated source code is included as a supplement) represents a proof of concept to encourage future work that takes advantage of recent advances in computing power to combine models of ecology, evolution, and earth history to better explain the abundance and distribution of species over time.

Comparative genomic de-convolution of the cotton genome revealed a decaploid ancestor and widespread chromosomal fractionation.

PubMed

Wang, Xiyin; Guo, Hui; Wang, Jinpeng; Lei, Tianyu; Liu, Tao; Wang, Zhenyi; Li, Yuxian; Lee, Tae-Ho; Li, Jingping; Tang, Haibao; Jin, Dianchuan; Paterson, Andrew H

2016-02-01

The 'apparently' simple genomes of many angiosperms mask complex evolutionary histories. The reference genome sequence for cotton (Gossypium spp.) revealed a ploidy change of a complexity unprecedented to date, indeed that could not be distinguished as to its exact dosage. Herein, by developing several comparative, computational and statistical approaches, we revealed a 5× multiplication in the cotton lineage of an ancestral genome common to cotton and cacao, and proposed evolutionary models to show how such a decaploid ancestor formed. The c. 70% gene loss necessary to bring the ancestral decaploid to its current gene count appears to fit an approximate geometrical model; that is, although many genes may be lost by single-gene deletion events, some may be lost in groups of consecutive genes. Gene loss following cotton decaploidy has largely just reduced gene copy numbers of some homologous groups. We designed a novel approach to deconvolute layers of chromosome homology, providing definitive information on gene orthology and paralogy across broad evolutionary distances, both of fundamental value and serving as an important platform to support further studies in and beyond cotton and genomics communities. No claim to original US government works. New Phytologist © 2015 New Phytologist Trust.

ON THE CHALLENGING VARIABILITY OF LS IV-14{sup 0}116: PULSATIONAL INSTABILITIES EXCITED BY THE {epsilon}-MECHANISM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller Bertolami, M. M.; Corsico, A. H.; Althaus, L. G., E-mail: mmiller@fcaglp.unlp.edu.ar

2011-11-01

We investigate the pulsation driving mechanism responsible for the long-period photometric variations observed in LS IV-14{sup 0}116, a subdwarf B star showing a He-enriched atmospheric composition. To this end, we perform detailed nonadiabatic pulsation computations over fully evolutionary post-He-core-flash stellar structure models, appropriate for hot subdwarf stars at evolutionary phases previous to the He-core burning stage. We found that the variability of LS IV-14{sup 0}116 can be attributed to non-radial g-mode pulsations excited by the {epsilon}-mechanism acting in the He-burning shells that appear before the star settles in the He-core burning stage. Even more interestingly, our results show that LSmore » IV-14{sup 0}116 could be the first known pulsating star in which the {epsilon}-mechanism of mode excitation is operating. Last but not the least, we find that the period range of destabilized modes is sensitive to the exact location of the burning shell, something that might help in distinguishing between the different evolutionary scenarios proposed for the formation of this star.« less

Signatures of microevolutionary processes in phylogenetic patterns.

PubMed

Costa, Carolina L N; Lemos-Costa, Paula; Marquitti, Flavia M D; Fernandes, Lucas D; Ramos, Marlon F; Schneider, David M; Martins, Ayana B; Aguiar, Marcus A M

2018-06-23

Phylogenetic trees are representations of evolutionary relationships among species and contain signatures of the processes responsible for the speciation events they display. Inferring processes from tree properties, however, is challenging. To address this problem we analysed a spatially-explicit model of speciation where genome size and mating range can be controlled. We simulated parapatric and sympatric (narrow and wide mating range, respectively) radiations and constructed their phylogenetic trees, computing structural properties such as tree balance and speed of diversification. We showed that parapatric and sympatric speciation are well separated by these structural tree properties. Balanced trees with constant rates of diversification only originate in sympatry and genome size affected both the balance and the speed of diversification of the simulated trees. Comparison with empirical data showed that most of the evolutionary radiations considered to have developed in parapatry or sympatry are in good agreement with model predictions. Even though additional forces other than spatial restriction of gene flow, genome size, and genetic incompatibilities, do play a role in the evolution of species formation, the microevolutionary processes modeled here capture signatures of the diversification pattern of evolutionary radiations, regarding the symmetry and speed of diversification of lineages.

Advances in understanding tumour evolution through single-cell sequencing.

PubMed

Kuipers, Jack; Jahn, Katharina; Beerenwinkel, Niko

2017-04-01

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Mathematics and evolutionary biology make bioinformatics education comprehensible.

PubMed

Jungck, John R; Weisstein, Anton E

2013-09-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes-the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software-the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a 'two-culture' problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses.

Mathematics and evolutionary biology make bioinformatics education comprehensible

PubMed Central

Weisstein, Anton E.

2013-01-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software—the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a ‘two-culture’ problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. PMID:23821621

Applying evolutionary psychology to a serious game about children's interpersonal conflict.

PubMed

Ingram, Gordon P D; Campos, Joana; Hondrou, Charline; Vasalou, Asimina; Martinho, Carlos; Joinson, Adam

2012-12-20

This article describes the use of evolutionary psychology to inform the design of a serious computer game aimed at improving 9-12-year-old children's conflict resolution skills. The design of the game will include dynamic narrative generation and emotional tagging, and there is a strong evolutionary rationale for the effect of both of these on conflict resolution. Gender differences will also be taken into consideration in designing the game. In interview research in schools in three countries (Greece, Portugal, and the UK) aimed at formalizing the game requirements, we found that gender differences varied in the extent to which they applied cross-culturally. Across the three countries, girls were less likely to talk about responding to conflict with physical aggression, talked more about feeling sad about conflict and about conflicts over friendship alliances, and talked less about conflicts in the context of sports or games. Predicted gender differences in anger and reconciliation were not found. Results are interpreted in terms of differing underlying models of friendship that are motivated by parental investment theory. This research will inform the design of the themes that we use in game scenarios for both girls and boys.

Turbomachinery Airfoil Design Optimization Using Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.

Biocontainment of genetically modified organisms by synthetic protein design

PubMed Central

Mandell, Daniel J.; Lajoie, Marc J.; Mee, Michael T.; Takeuchi, Ryo; Kuznetsov, Gleb; Norville, Julie E.; Gregg, Christopher J.; Stoddard, Barry L.; Church, George M.

2015-01-01

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient either because they impose evolutionary pressure on the organism to eject the safeguard, because they can be circumvented by environmentally available compounds, or because they can be overcome by horizontal gene transfer (HGT). Here we computationally redesign essential enzymes in the first organism possessing an altered genetic code to confer metabolic dependence on nonstandard amino acids for survival. The resulting GMOs cannot metabolically circumvent their biocontainment mechanisms using environmentally available compounds, and they exhibit unprecedented resistance to evolutionary escape via mutagenesis and HGT. This work provides a foundation for safer GMOs that are isolated from natural ecosystems by reliance on synthetic metabolites. PMID:25607366

Evolutionary programming-based univector field navigation method for past mobile robots.

PubMed

Kim, Y J; Kim, J H; Kwon, D S

2001-01-01

Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocity. To solve these problems this paper proposes a novel univector field method for fast mobile robot navigation which introduces a normalized two dimensional vector field. The method provides fast moving robots with the desired posture at the target position and obstacle avoidance. To obtain the sub-optimal vector field, a function approximator is used and trained by evolutionary programming. Two kinds of vector fields are trained, one for the final posture acquisition and the other for obstacle avoidance. Computer simulations and real experiments are carried out for a fast moving mobile robot to demonstrate the effectiveness of the proposed scheme.

A synopsis of test results and knowledge gained from the Phase-0 CSI evolutionary model

NASA Technical Reports Server (NTRS)

Belvin, W. Keith; Elliott, Kenny B.; Horta, Lucas G.

1993-01-01

The Phase-0 CSI Evolutionary Model (CEM) is a testbed for the study of space platform global line-of-sight (LOS) pointing. Now that the tests have been completed, a summary of hardware and closed-loop test experiences is necessary to insure a timely dissemination of the knowledge gained. The testbed is described and modeling experiences are presented followed by a summary of the research performed by various investigators. Some early lessons on implementing the closed-loop controllers are described with particular emphasis on real-time computing requirements. A summary of closed-loop studies and a synopsis of test results are presented. Plans for evolving the CEM from phase 0 to phases 1 and 2 are also described. Subsequently, a summary of knowledge gained from the design and testing of the Phase-0 CEM is made.

Properties of LEGUS Clusters Obtained with Different Massive-Star Evolutionary Tracks

NASA Astrophysics Data System (ADS)

Wofford, A.; Charlot, S.; Eldridge, J. J.

We compute spectral libraries for populations of coeval stars using state-of-the-art massive-star evolutionary tracks that account for different astrophysics including rotation and close-binarity. Our synthetic spectra account for stellar and nebular contributions. We use our models to obtain E(B - V ), age, and mass for six clusters in spiral galaxy NGC 1566, which have ages of < 50 Myr and masses of > 5 x 104M⊙ according to standard models. NGC 1566 was observed from the NUV to the I-band as part of the imaging Treasury HST program LEGUS: Legacy Extragalactic UV Survey. We aim to establish i) if the models provide reasonable fits to the data, ii) how well the models and photometry are able to constrain the cluster properties, and iii) how different the properties obtained with different models are.

Biocontainment of genetically modified organisms by synthetic protein design.

PubMed

Mandell, Daniel J; Lajoie, Marc J; Mee, Michael T; Takeuchi, Ryo; Kuznetsov, Gleb; Norville, Julie E; Gregg, Christopher J; Stoddard, Barry L; Church, George M

2015-02-05

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. Here we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.ΔA) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass their biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. This work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.

Biocontainment of genetically modified organisms by synthetic protein design

NASA Astrophysics Data System (ADS)

Mandell, Daniel J.; Lajoie, Marc J.; Mee, Michael T.; Takeuchi, Ryo; Kuznetsov, Gleb; Norville, Julie E.; Gregg, Christopher J.; Stoddard, Barry L.; Church, George M.

2015-02-01

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. Here we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.ΔA) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass their biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. This work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.

The protein-protein interface evolution acts in a similar way to antibody affinity maturation.

PubMed

Li, Bohua; Zhao, Lei; Wang, Chong; Guo, Huaizu; Wu, Lan; Zhang, Xunming; Qian, Weizhu; Wang, Hao; Guo, Yajun

2010-02-05

Understanding the evolutionary mechanism that acts at the interfaces of protein-protein complexes is a fundamental issue with high interest for delineating the macromolecular complexes and networks responsible for regulation and complexity in biological systems. To investigate whether the evolution of protein-protein interface acts in a similar way as antibody affinity maturation, we incorporated evolutionary information derived from antibody affinity maturation with common simulation techniques to evaluate prediction success rates of the computational method in affinity improvement in four different systems: antibody-receptor, antibody-peptide, receptor-membrane ligand, and receptor-soluble ligand. It was interesting to find that the same evolutionary information could improve the prediction success rates in all the four protein-protein complexes with an exceptional high accuracy (>57%). One of the most striking findings in our present study is that not only in the antibody-combining site but in other protein-protein interfaces almost all of the affinity-enhancing mutations are located at the germline hotspot sequences (RGYW or WA), indicating that DNA hot spot mechanisms may be widely used in the evolution of protein-protein interfaces. Our data suggest that the evolution of distinct protein-protein interfaces may use the same basic strategy under selection pressure to maintain interactions. Additionally, our data indicate that classical simulation techniques incorporating the evolutionary information derived from in vivo antibody affinity maturation can be utilized as a powerful tool to improve the binding affinity of protein-protein complex with a high accuracy.

Molecular Evolution of the Non-Coding Eosinophil Granule Ontogeny Transcript

PubMed Central

Rose, Dominic; Stadler, Peter F.

2011-01-01

Eukaryotic genomes are pervasively transcribed. A large fraction of the transcriptional output consists of long, mRNA-like, non-protein-coding transcripts (mlncRNAs). The evolutionary history of mlncRNAs is still largely uncharted territory. In this contribution, we explore in detail the evolutionary traces of the eosinophil granule ontogeny transcript (EGOT), an experimentally confirmed representative of an abundant class of totally intronic non-coding transcripts (TINs). EGOT is located antisense to an intron of the ITPR1 gene. We computationally identify putative EGOT orthologs in the genomes of 32 different amniotes, including orthologs from primates, rodents, ungulates, carnivores, afrotherians, and xenarthrans, as well as putative candidates from basal amniotes, such as opossum or platypus. We investigate the EGOT gene phylogeny, analyze patterns of sequence conservation, and the evolutionary conservation of the EGOT gene structure. We show that EGO-B, the spliced isoform, may be present throughout the placental mammals, but most likely dates back even further. We demonstrate here for the first time that the whole EGOT locus is highly structured, containing several evolutionary conserved, and thermodynamic stable secondary structures. Our analyses allow us to postulate novel functional roles of a hitherto poorly understood region at the intron of EGO-B which is highly conserved at the sequence level. The region contains a novel ITPR1 exon and also conserved RNA secondary structures together with a conserved TATA-like element, which putatively acts as a promoter of an independent regulatory element. PMID:22303364

Evolutionary and Functional Relationships in the Truncated Hemoglobin Family.

PubMed

Bustamante, Juan P; Radusky, Leandro; Boechi, Leonardo; Estrin, Darío A; Ten Have, Arjen; Martí, Marcelo A

2016-01-01

Predicting function from sequence is an important goal in current biological research, and although, broad functional assignment is possible when a protein is assigned to a family, predicting functional specificity with accuracy is not straightforward. If function is provided by key structural properties and the relevant properties can be computed using the sequence as the starting point, it should in principle be possible to predict function in detail. The truncated hemoglobin family presents an interesting benchmark study due to their ubiquity, sequence diversity in the context of a conserved fold and the number of characterized members. Their functions are tightly related to O2 affinity and reactivity, as determined by the association and dissociation rate constants, both of which can be predicted and analyzed using in-silico based tools. In the present work we have applied a strategy, which combines homology modeling with molecular based energy calculations, to predict and analyze function of all known truncated hemoglobins in an evolutionary context. Our results show that truncated hemoglobins present conserved family features, but that its structure is flexible enough to allow the switch from high to low affinity in a few evolutionary steps. Most proteins display moderate to high oxygen affinities and multiple ligand migration paths, which, besides some minor trends, show heterogeneous distributions throughout the phylogenetic tree, again suggesting fast functional adaptation. Our data not only deepens our comprehension of the structural basis governing ligand affinity, but they also highlight some interesting functional evolutionary trends.

Evolutionary and Functional Relationships in the Truncated Hemoglobin Family

PubMed Central

Bustamante, Juan P.; Radusky, Leandro; Boechi, Leonardo; Estrin, Darío A.; ten Have, Arjen; Martí, Marcelo A.

2016-01-01

Predicting function from sequence is an important goal in current biological research, and although, broad functional assignment is possible when a protein is assigned to a family, predicting functional specificity with accuracy is not straightforward. If function is provided by key structural properties and the relevant properties can be computed using the sequence as the starting point, it should in principle be possible to predict function in detail. The truncated hemoglobin family presents an interesting benchmark study due to their ubiquity, sequence diversity in the context of a conserved fold and the number of characterized members. Their functions are tightly related to O2 affinity and reactivity, as determined by the association and dissociation rate constants, both of which can be predicted and analyzed using in-silico based tools. In the present work we have applied a strategy, which combines homology modeling with molecular based energy calculations, to predict and analyze function of all known truncated hemoglobins in an evolutionary context. Our results show that truncated hemoglobins present conserved family features, but that its structure is flexible enough to allow the switch from high to low affinity in a few evolutionary steps. Most proteins display moderate to high oxygen affinities and multiple ligand migration paths, which, besides some minor trends, show heterogeneous distributions throughout the phylogenetic tree, again suggesting fast functional adaptation. Our data not only deepens our comprehension of the structural basis governing ligand affinity, but they also highlight some interesting functional evolutionary trends. PMID:26788940

Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hot spot: a statistical phylogeographic study using a low-dispersal organism.

PubMed

Álvarez-Presas, M; Sánchez-Gracia, A; Carbayo, F; Rozas, J; Riutort, M

2014-06-01

The relative importance of the processes that generate and maintain biodiversity is a major and controversial topic in evolutionary biology with large implications for conservation management. The Atlantic Forest of Brazil, one of the world's richest biodiversity hot spots, is severely damaged by human activities. To formulate an efficient conservation policy, a good understanding of spatial and temporal biodiversity patterns and their underlying evolutionary mechanisms is required. With this aim, we performed a comprehensive phylogeographic study using a low-dispersal organism, the land planarian species Cephaloflexa bergi (Platyhelminthes, Tricladida). Analysing multi-locus DNA sequence variation under the Approximate Bayesian Computation framework, we evaluated two scenarios proposed to explain the diversity of Southern Atlantic Forest (SAF) region. We found that most sampled localities harbour high levels of genetic diversity, with lineages sharing common ancestors that predate the Pleistocene. Remarkably, we detected the molecular hallmark of the isolation-by-distance effect and little evidence of a recent colonization of SAF localities; nevertheless, some populations might result from very recent secondary contacts. We conclude that extant SAF biodiversity originated and has been shaped by complex interactions between ancient geological events and more recent evolutionary processes, whereas Pleistocene climate changes had a minor influence in generating present-day diversity. We also demonstrate that land planarians are an advantageous biological model for making phylogeographic and, particularly, fine-scale evolutionary inferences, and propose appropriate conservation policies.

Using traveling salesman problem algorithms for evolutionary tree construction.

PubMed

Korostensky, C; Gonnet, G H

2000-07-01

The construction of evolutionary trees is one of the major problems in computational biology, mainly due to its complexity. We present a new tree construction method that constructs a tree with minimum score for a given set of sequences, where the score is the amount of evolution measured in PAM distances. To do this, the problem of tree construction is reduced to the Traveling Salesman Problem (TSP). The input for the TSP algorithm are the pairwise distances of the sequences and the output is a circular tour through the optimal, unknown tree plus the minimum score of the tree. The circular order and the score can be used to construct the topology of the optimal tree. Our method can be used for any scoring function that correlates to the amount of changes along the branches of an evolutionary tree, for instance it could also be used for parsimony scores, but it cannot be used for least squares fit of distances. A TSP solution reduces the space of all possible trees to 2n. Using this order, we can guarantee that we reconstruct a correct evolutionary tree if the absolute value of the error for each distance measurement is smaller than f2.gif" BORDER="0">, where f3.gif" BORDER="0">is the length of the shortest edge in the tree. For data sets with large errors, a dynamic programming approach is used to reconstruct the tree. Finally simulations and experiments with real data are shown.

A comprehensive overview of the applications of artificial life.

PubMed

Kim, Kyung-Joong; Cho, Sung-Bae

2006-01-01

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

A tale of three bio-inspired computational approaches

NASA Astrophysics Data System (ADS)

Schaffer, J. David

2014-05-01

I will provide a high level walk-through for three computational approaches derived from Nature. First, evolutionary computation implements what we may call the "mother of all adaptive processes." Some variants on the basic algorithms will be sketched and some lessons I have gleaned from three decades of working with EC will be covered. Then neural networks, computational approaches that have long been studied as possible ways to make "thinking machines", an old dream of man's, and based upon the only known existing example of intelligence. Then, a little overview of attempts to combine these two approaches that some hope will allow us to evolve machines we could never hand-craft. Finally, I will touch on artificial immune systems, Nature's highly sophisticated defense mechanism, that has emerged in two major stages, the innate and the adaptive immune systems. This technology is finding applications in the cyber security world.

Generative Representations for Computer-Automated Design Systems

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2004-01-01

With the increasing computational power of Computers, software design systems are progressing from being tools for architects and designers to express their ideas to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design programs is the representation with which they encode designs. If the representation cannot encode a certain design, then the design program cannot produce it. Similarly, a poor representation makes some types of designs extremely unlikely to be created. Here we define generative representations as those representations which can create and reuse organizational units within a design and argue that reuse is necessary for design systems to scale to more complex and interesting designs. To support our argument we describe GENRE, an evolutionary design program that uses both a generative and a non-generative representation, and compare the results of evolving designs with both types of representations.

Medical physics: some recollections in diagnostic X-ray imaging and therapeutic radiology.

PubMed

Gray, J E; Orton, C G

2000-12-01

Medical physics has changed dramatically since 1895. There was a period of slow evolutionary change during the first 70 years after Roentgen's discovery of x rays. With the advent of the computer, however, both diagnostic and therapeutic radiology have undergone rapid growth and changes. Technologic advances such as computed tomography and magnetic resonance imaging in diagnostic imaging and three-dimensional treatment planning systems, stereotactic radiosurgery, and intensity modulated radiation therapy in radiation oncology have resulted in substantial changes in medical physics. These advances have improved diagnostic imaging and radiation therapy while expanding the need for better educated and experienced medical physics staff.

New Frontiers in Language Evolution and Development.

PubMed

Oller, D Kimbrough; Dale, Rick; Griebel, Ulrike

2016-04-01

This article introduces the Special Issue and its focus on research in language evolution with emphasis on theory as well as computational and robotic modeling. A key theme is based on the growth of evolutionary developmental biology or evo-devo. The Special Issue consists of 13 articles organized in two sections: A) Theoretical foundations and B) Modeling and simulation studies. All the papers are interdisciplinary in nature, encompassing work in biological and linguistic foundations for the study of language evolution as well as a variety of computational and robotic modeling efforts shedding light on how language may be developed and may have evolved. Copyright © 2016 Cognitive Science Society, Inc.

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

Evolution of Autonomous Self-Righting Behaviors for Articulated Nanorovers

NASA Technical Reports Server (NTRS)

Tunstel, Edward

1999-01-01

Miniature rovers with articulated mobility mechanisms are being developed for planetary surface exploration on Mars and small solar system bodies. These vehicles are designed to be capable of autonomous recovery from overturning during surface operations. This paper describes a computational means of developing motion behaviors that achieve the autonomous recovery function. It proposes a control software design approach aimed at reducing the effort involved in developing self-righting behaviors. The approach is based on the integration of evolutionary computing with a dynamics simulation environment for evolving and evaluating motion behaviors. The automated behavior design approach is outlined and its underlying genetic programming infrastructure is described.

Computational optimization and biological evolution.

PubMed

Goryanin, Igor

2010-10-01

Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

Adaptive Fuzzy Systems in Computational Intelligence

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

DTIC Science & Technology

2009-03-10

pp. 594-606. 8. Inverse Approaches to Drying of Thin Bodies With Significant Shrinkage Effects (with G. H. Kanevce, L. P. Kanevce, V. B. Mitrevski ...Kanevce, L. Kanevce, V. Mitrevski ), ICCES󈧌: International Conference on Computational & Experimental Engineering and Sciences, Honolulu, Hawaii, March 17...Miami Beach, FL, April 16-18, 2007. 16. Inverse Approaches to Drying of Sliced Foods (with Kanevce, G. H., Kanevce, Lj. P., and Mitrevski , V. B

Parameters Estimation For A Patellofemoral Joint Of A Human Knee Using A Vector Method

NASA Astrophysics Data System (ADS)

Ciszkiewicz, A.; Knapczyk, J.

2015-08-01

Position and displacement analysis of a spherical model of a human knee joint using the vector method was presented. Sensitivity analysis and parameter estimation were performed using the evolutionary algorithm method. Computer simulations for the mechanism with estimated parameters proved the effectiveness of the prepared software. The method itself can be useful when solving problems concerning the displacement and loads analysis in the knee joint.

A Biologically Based Approach to the Mutation of Code

DTIC Science & Technology

1999-09-01

SECOND POSITION U c A G u phenyl- alanine serine tyro sine cysteine U C leucine stop stop A stop tryptophan G o c leucine proline histidine...individuals, is a means to explore the fitness landscape and find more solutions. B. INTRODUCTION TO EVOLUTIONARY COMPUTATION There are several...respiration and are responsible for making adenosine triphosphate (ATP), which is the cellular energy source. G. The Lysosomes These organelles are

Tell Me How to Do This Thing Called Design! Practical Application of Complexity Theory to Military Operations

DTIC Science & Technology

2011-04-08

into how economics, information theory and computer science, psychology, sociology, evolutionary biology, physics (quantum mechanics) and cosmology ...include knowledge and definition of “self” (as “self” is part of the environment) and the shared experience and perspective of others  That...including information, entropy, quantum behavior, and cosmological progress In short I assume the above and therefore my recommendations could be

Recent advances in bird sperm morphometric analysis and its role in male gamete characterization and reproduction technologies

PubMed Central

Santiago-Moreno, Julian; Esteso, Milagros Cristina; Villaverde-Morcillo, Silvia; Toledano-Díaz, Adolfo; Castaño, Cristina; Velázquez, Rosario; López-Sebastián, Antonio; Goya, Agustín López; Martínez, Javier Gimeno

2016-01-01

Postcopulatory sexual selection through sperm competition may be an important evolutionary force affecting many reproductive traits, including sperm morphometrics. Environmental factors such as pollutants, pesticides, and climate change may affect different sperm traits, and thus reproduction, in sensitive bird species. Many sperm-handling processes used in assisted reproductive techniques may also affect the size of sperm cells. The accurately measured dimensions of sperm cell structures (especially the head) can thus be used as indicators of environmental influences, in improving our understanding of reproductive and evolutionary strategies, and for optimizing assisted reproductive techniques (e.g., sperm cryopreservation) for use with birds. Computer-assisted sperm morphometry analysis (CASA-Morph) provides an accurate and reliable method for assessing sperm morphometry, reducing the problem of subjectivity associated with human visual assessment. Computerized systems have been standardized for use with semen from different mammalian species. Avian spermatozoa, however, are filiform, limiting their analysis with such systems, which were developed to examine the approximately spherical heads of mammalian sperm cells. To help overcome this, the standardization of staining techniques to be used in computer-assessed light microscopical methods is a priority. The present review discusses these points and describes the sperm morphometric characteristics of several wild and domestic bird species. PMID:27678467

Applying Computerized-Scoring Models of Written Biological Explanations across Courses and Colleges: Prospects and Limitations

PubMed Central

Ha, Minsu; Nehm, Ross H.; Urban-Lurain, Mark; Merrill, John E.

2011-01-01

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students’ written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors’ and nonmajors’ written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of “near-perfect” agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations. PMID:22135372

An efficient non-dominated sorting method for evolutionary algorithms.

PubMed

Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F

2008-01-01

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

Applying computerized-scoring models of written biological explanations across courses and colleges: prospects and limitations.

PubMed

Ha, Minsu; Nehm, Ross H; Urban-Lurain, Mark; Merrill, John E

2011-01-01

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students' written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors' and nonmajors' written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of "near-perfect" agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations.

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Efficient Evaluation Functions for Multi-Rover Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian; Tumer, Kagan

2004-01-01

Evolutionary computation can be a powerful tool in cresting a control policy for a single agent receiving local continuous input. This paper extends single-agent evolutionary computation to multi-agent systems, where a collection of agents strives to maximize a global fitness evaluation function that rates the performance of the entire system. This problem is solved in a distributed manner, where each agent evolves its own population of neural networks that are used as the control policies for the agent. Each agent evolves its population using its own agent-specific fitness evaluation function. We propose to create these agent-specific evaluation functions using the theory of collectives to avoid the coordination problem where each agent evolves a population that maximizes its own fitness function, yet the system has a whole achieves low values of the global fitness function. Instead we will ensure that each fitness evaluation function is both "aligned" with the global evaluation function and is "learnable," i.e., the agents can readily see how their behavior affects their evaluation function. We then show how these agent-specific evaluation functions outperform global evaluation methods by up to 600% in a domain where a set of rovers attempt to maximize the amount of information observed while navigating through a simulated environment.

Alignment-free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer.

PubMed

Bernard, Guillaume; Chan, Cheong Xin; Ragan, Mark A

2016-07-01

Alignment-free (AF) approaches have recently been highlighted as alternatives to methods based on multiple sequence alignment in phylogenetic inference. However, the sensitivity of AF methods to genome-scale evolutionary scenarios is little known. Here, using simulated microbial genome data we systematically assess the sensitivity of nine AF methods to three important evolutionary scenarios: sequence divergence, lateral genetic transfer (LGT) and genome rearrangement. Among these, AF methods are most sensitive to the extent of sequence divergence, less sensitive to low and moderate frequencies of LGT, and most robust against genome rearrangement. We describe the application of AF methods to three well-studied empirical genome datasets, and introduce a new application of the jackknife to assess node support. Our results demonstrate that AF phylogenomics is computationally scalable to multi-genome data and can generate biologically meaningful phylogenies and insights into microbial evolution.

First orbital solution and evolutionary state for the newly discovered eclipsing binaries USNO-B1.0 1091-0130715 and GSC-03449-0680

NASA Astrophysics Data System (ADS)

Elkhateeb, M. M.; Nouh, M. I.; Nelson, R. H.

2015-02-01

A first photometric study for the newly discovered systems USNO-B1.0 1091-0130715 and GSC-03449-0680 was carried out by means of recent a windows interface version of the Wilson and Devinney code based on model atmospheres by Kurucz (1993). The accepted models reveal some absolute parameters for both systems, which are used in deriving the spectral type of the system components and their evolutionary status. Distances to each systems and physical properties were estimated. Comparisons of the computed physical parameters with stellar models are discussed. The components of the system USNO-B1.0 1091-0130715 and the primary of the system GSC-03449-0680 are found to be on or near the ZAMS track, while the secondary of GSC-03449-0680 system found to be severely under luminous and too cool compared to its ZAMS mass.

Aerodynamic Shape Optimization Using Hybridized Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2003-01-01

An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.

Practical aspects of protein co-evolution.

PubMed

Ochoa, David; Pazos, Florencio

2014-01-01

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Practical aspects of protein co-evolution

PubMed Central

Ochoa, David; Pazos, Florencio

2014-01-01

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these. PMID:25364721

Resource-constrained scheduling with hard due windows and rejection penalties

NASA Astrophysics Data System (ADS)

Garcia, Christopher

2016-09-01

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.

Biocontainment of genetically modified organisms by synthetic protein design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mandell, Daniel J.; Lajoie, Marc J.; Mee, Michael T.

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. In this paper, we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.ΔA) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass theirmore » biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. Finally, this work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.« less

Evolutionary scalpels for dissecting tumor ecosystems

PubMed Central

Rosenbloom, Daniel I. S.; Camara, Pablo G.; Chu, Tim; Rabadan, Raul

2017-01-01

Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of “hit hard, hit early” treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles. PMID:27923679

Characterizing Conformational Dynamics of Proteins Using Evolutionary Couplings.

PubMed

Feng, Jiangyan; Shukla, Diwakar

2018-01-25

Understanding of protein conformational dynamics is essential for elucidating molecular origins of protein structure-function relationship. Traditionally, reaction coordinates, i.e., some functions of protein atom positions and velocities have been used to interpret the complex dynamics of proteins obtained from experimental and computational approaches such as molecular dynamics simulations. However, it is nontrivial to identify the reaction coordinates a priori even for small proteins. Here, we evaluate the power of evolutionary couplings (ECs) to capture protein dynamics by exploring their use as reaction coordinates, which can efficiently guide the sampling of a conformational free energy landscape. We have analyzed 10 diverse proteins and shown that a few ECs are sufficient to characterize complex conformational dynamics of proteins involved in folding and conformational change processes. With the rapid strides in sequencing technology, we expect that ECs could help identify reaction coordinates a priori and enhance the sampling of the slow dynamical process associated with protein folding and conformational change.

New phases of osmium carbide from evolutionary algorithm and ab initio computations

NASA Astrophysics Data System (ADS)

Fadda, Alessandro; Fadda, Giuseppe

2017-09-01

New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry by using an ad hoc search space and genetic operators. The new OsC2 and Os2C structures have a lower enthalpy than any known so far. Moreover, the layered pattern of OsC2 serves as a blueprint for building new crystals by adding or removing layers of carbon and/or osmium and generating many other Os  +  C structures like Os2C, OsC, OsC2 and OsC4. These again have a lower enthalpy than all the investigated structures, including those of the present work. The mechanical, vibrational and electronic properties are discussed as well.

Properties of ecosystems that are vulnerable during eco-fusion

PubMed Central

Yoshida, Katsuhiko; Tokita, Kei

2015-01-01

When two ecosystems with separate evolutionary histories come into contact (eco-fusion), reciprocal invasions occur during their fusion. Asymmetries in the migration direction or extinction rate then occur (e.g., during the Great American Biotic Interchange, GABI). Hypotheses have been proposed to describe this process, but the ecosystem properties have not been adequately discussed. To identify the ecosystem properties that create vulnerability to species loss during eco-fusion, we conducted computer simulations of the fusion of ecosystems with independent evolutionary histories. With asymmetrical species extinction rates, the ecosystem with a higher extinction rate had a shorter food chain, a higher ratio of animal species to plant species, and a lower ratio of carnivores to herbivores. Most ecosystems that have undergone isolated evolution are vulnerable. These results may explain the vulnerability of South America's ecosystem during the GABI and that of modern Australia. PMID:25631294

Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution.

PubMed

Uchi, Ryutaro; Takahashi, Yusuke; Niida, Atsushi; Shimamura, Teppei; Hirata, Hidenari; Sugimachi, Keishi; Sawada, Genta; Iwaya, Takeshi; Kurashige, Junji; Shinden, Yoshiaki; Iguchi, Tomohiro; Eguchi, Hidetoshi; Chiba, Kenichi; Shiraishi, Yuichi; Nagae, Genta; Yoshida, Kenichi; Nagata, Yasunobu; Haeno, Hiroshi; Yamamoto, Hirofumi; Ishii, Hideshi; Doki, Yuichiro; Iinuma, Hisae; Sasaki, Shin; Nagayama, Satoshi; Yamada, Kazutaka; Yachida, Shinichi; Kato, Mamoru; Shibata, Tatsuhiro; Oki, Eiji; Saeki, Hiroshi; Shirabe, Ken; Oda, Yoshinao; Maehara, Yoshihiko; Komune, Shizuo; Mori, Masaki; Suzuki, Yutaka; Yamamoto, Ken; Aburatani, Hiroyuki; Ogawa, Seishi; Miyano, Satoru; Mimori, Koshi

2016-02-01

Understanding intratumor heterogeneity is clinically important because it could cause therapeutic failure by fostering evolutionary adaptation. To this end, we profiled the genome and epigenome in multiple regions within each of nine colorectal tumors. Extensive intertumor heterogeneity is observed, from which we inferred the evolutionary history of the tumors. First, clonally shared alterations appeared, in which C>T transitions at CpG site and CpG island hypermethylation were relatively enriched. Correlation between mutation counts and patients' ages suggests that the early-acquired alterations resulted from aging. In the late phase, a parental clone was branched into numerous subclones. Known driver alterations were observed frequently in the early-acquired alterations, but rarely in the late-acquired alterations. Consistently, our computational simulation of the branching evolution suggests that extensive intratumor heterogeneity could be generated by neutral evolution. Collectively, we propose a new model of colorectal cancer evolution, which is useful for understanding and confronting this heterogeneous disease.

Random Evolutionary Dynamics Driven by Fitness and House-of-Cards Mutations: Sampling Formulae

NASA Astrophysics Data System (ADS)

Huillet, Thierry E.

2017-07-01

We first revisit the multi-allelic mutation-fitness balance problem, especially when mutations obey a house of cards condition, where the discrete-time deterministic evolutionary dynamics of the allelic frequencies derives from a Shahshahani potential. We then consider multi-allelic Wright-Fisher stochastic models whose deviation to neutrality is from the Shahshahani mutation/selection potential. We next focus on the weak selection, weak mutation cases and, making use of a Gamma calculus, we compute the normalizing partition functions of the invariant probability densities appearing in their Wright-Fisher diffusive approximations. Using these results, generalized Ewens sampling formulae (ESF) from the equilibrium distributions are derived. We start treating the ESF in the mixed mutation/selection potential case and then we restrict ourselves to the ESF in the simpler house-of-cards mutations only situation. We also address some issues concerning sampling problems from infinitely-many alleles weak limits.

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

Biocontainment of genetically modified organisms by synthetic protein design

DOE PAGES

Mandell, Daniel J.; Lajoie, Marc J.; Mee, Michael T.; ...

2015-01-21

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. In this paper, we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.ΔA) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass theirmore » biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. Finally, this work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.« less

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

NASA Astrophysics Data System (ADS)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

Naturally occurring tumours in the basal metazoan Hydra.

PubMed

Domazet-Lošo, Tomislav; Klimovich, Alexander; Anokhin, Boris; Anton-Erxleben, Friederike; Hamm, Mailin J; Lange, Christina; Bosch, Thomas C G

2014-06-24

The molecular nature of tumours is well studied in vertebrates, although their evolutionary origin remains unknown. In particular, there is no evidence for naturally occurring tumours in pre-bilaterian animals, such as sponges and cnidarians. This is somewhat surprising given that recent computational studies have predicted that most metazoans might be prone to develop tumours. Here we provide first evidence for naturally occurring tumours in two species of Hydra. Histological, cellular and molecular data reveal that these tumours are transplantable and might originate by differentiation arrest of female gametes. Growth of tumour cells is independent from the cellular environment. Tumour-bearing polyps have significantly reduced fitness. In addition, Hydra tumours show a greatly altered transcriptome that mimics expression shifts in vertebrate cancers. Therefore, this study shows that spontaneous tumours have deep evolutionary roots and that early branching animals may be informative in revealing the fundamental mechanisms of tumorigenesis.

Improving the sampling efficiency of Monte Carlo molecular simulations: an evolutionary approach

NASA Astrophysics Data System (ADS)

Leblanc, Benoit; Braunschweig, Bertrand; Toulhoat, Hervé; Lutton, Evelyne

We present a new approach in order to improve the convergence of Monte Carlo (MC) simulations of molecular systems belonging to complex energetic landscapes: the problem is redefined in terms of the dynamic allocation of MC move frequencies depending on their past efficiency, measured with respect to a relevant sampling criterion. We introduce various empirical criteria with the aim of accounting for the proper convergence in phase space sampling. The dynamic allocation is performed over parallel simulations by means of a new evolutionary algorithm involving 'immortal' individuals. The method is bench marked with respect to conventional procedures on a model for melt linear polyethylene. We record significant improvement in sampling efficiencies, thus in computational load, while the optimal sets of move frequencies are liable to allow interesting physical insights into the particular systems simulated. This last aspect should provide a new tool for designing more efficient new MC moves.

Measuring, Enabling and Comparing Modularity, Regularity and Hierarchy in Evolutionary Design

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2005-01-01

For computer-automated design systems to scale to complex designs they must be able to produce designs that exhibit the characteristics of modularity, regularity and hierarchy - characteristics that are found both in man-made and natural designs. Here we claim that these characteristics are enabled by implementing the attributes of combination, control-flow and abstraction in the representation. To support this claim we use an evolutionary algorithm to evolve solutions to different sizes of a table design problem using five different representations, each with different combinations of modularity, regularity and hierarchy enabled and show that the best performance happens when all three of these attributes are enabled. We also define metrics for modularity, regularity and hierarchy in design encodings and demonstrate that high fitness values are achieved with high values of modularity, regularity and hierarchy and that there is a positive correlation between increases in fitness and increases in modularity. regularity and hierarchy.

Evolutionary fate of memory-one strategies in repeated prisoner's dilemma game in structured populations

NASA Astrophysics Data System (ADS)

Liu, Xu-Sheng; Wu, Zhi-Xi; Chen, Michael Z. Q.; Guan, Jian-Yue

2017-07-01

We study evolutionary spatial prisoner's dilemma game involving a one-step memory mechanism of the individuals whenever making strategy updating. In particular, during the process of strategy updating, each individual keeps in mind all the outcome of the action pairs adopted by himself and each of his neighbors in the last interaction, and according to which the individuals decide what actions they will take in the next round. Computer simulation results imply that win-stay-lose-shift like strategy win out of the memory-one strategy set in the stationary state. This result is robust in a large range of the payoff parameter, and does not depend on the initial state of the system. Furthermore, theoretical analysis with mean field and quasi-static approximation predict the same result. Thus, our studies suggest that win-stay-lose-shift like strategy is a stable dominant strategy in repeated prisoner's dilemma game in homogeneous structured populations.

Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations.

PubMed

Liao, David; Tlsty, Thea D

2014-08-06

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

Computational analysis and functional expression of ancestral copepod luciferase.

PubMed

Takenaka, Yasuhiro; Noda-Ogura, Akiko; Imanishi, Tadashi; Yamaguchi, Atsushi; Gojobori, Takashi; Shigeri, Yasushi

2013-10-10

We recently reported the cDNA sequences of 11 copepod luciferases from the superfamily Augaptiloidea in the order Calanoida. They were classified into two groups, Metridinidae and Heterorhabdidae/Lucicutiidae families, by phylogenetic analyses. To elucidate the evolutionary processes, we have now further isolated 12 copepod luciferases from Augaptiloidea species (Metridia asymmetrica, Metridia curticauda, Pleuromamma scutullata, Pleuromamma xiphias, Lucicutia ovaliformis and Heterorhabdus tanneri). Codon-based synonymous/nonsynonymous tests of positive selection for 25 identified copepod luciferases suggested that positive Darwinian selection operated in the evolution of Heterorhabdidae luciferases, whereas two types of Metridinidae luciferases had diversified via neutral mechanism. By in silico analysis of the decoded amino acid sequences of 25 copepod luciferases, we inferred two protein sequences as ancestral copepod luciferases. They were expressed in HEK293 cells where they exhibited notable luciferase activity both in intracellular lysates and cultured media, indicating that the luciferase activity was established before evolutionary diversification of these copepod species. © 2013.

Instability of signaling resolution models of parent–offspring conflict

PubMed Central

Rodríguez-Gironés, Miguel A.; Enquist, Magnus; Cotton, Peter A.

1998-01-01

Recent signaling resolution models of parent–offspring conflict have provided an important framework for theoretical and empirical studies of communication and parental care. According to these models, signaling of need is stabilized by its cost. However, our computer simulations of the evolutionary dynamics of chick begging and parental investment show that in Godfray’s model the signaling equilibrium is evolutionarily unstable: populations that start at the signaling equilibrium quickly depart from it. Furthermore, the signaling and nonsignaling equilibria are linked by a continuum of equilibria where chicks above a certain condition do not signal and we show that, contrary to intuition, fitness increases monotonically as the proportion of young that signal decreases. This result forces us to reconsider much of the current literature on signaling of need and highlights the need to investigate the evolutionary stability of signaling equilibria based on the handicap principle. PMID:9539758

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: Challenges, opportunities, and research needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burgess-Herbert, Sarah L., E-mail: sarah.burgess@alum.mit.edu; Euling, Susan Y.

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionarymore » biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.« less

Phenotypic plasticity, the Baldwin effect, and the speeding up of evolution: the computational roots of an illusion.

PubMed

Santos, Mauro; Szathmáry, Eörs; Fontanari, José F

2015-04-21

An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance, with the turn-of-the-twentieth-century notion eventually labeled as the 'Baldwin effect' as one of the many ways in which the standard neo-Darwinian view can be turned around. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a trait. This gives the additional time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s claimed the Baldwin effect to happen in computer simulations, and avowed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. Evolutionary psychologists and linguists have invoked the (claimed) Baldwin effect to make wild assertions that should not be taken seriously. What the Baldwin effect needs are plausible case-histories. Copyright © 2015 Elsevier Ltd. All rights reserved.

Wind Farm Layout Optimization through a Crossover-Elitist Evolutionary Algorithm performed over a High Performing Analytical Wake Model

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Porté-Agel, Fernando

2017-04-01

Wind turbine wakes can significantly disrupt the performance of further downstream turbines in a wind farm, thus seriously limiting the overall wind farm power output. Such effect makes the layout design of a wind farm to play a crucial role on the whole performance of the project. An accurate definition of the wake interactions added to a computationally compromised layout optimization strategy can result in an efficient resource when addressing the problem. This work presents a novel soft-computing approach to optimize the wind farm layout by minimizing the overall wake effects that the installed turbines exert on one another. An evolutionary algorithm with an elitist sub-optimization crossover routine and an unconstrained (continuous) turbine positioning set up is developed and tested over an 80-turbine offshore wind farm over the North Sea off Denmark (Horns Rev I). Within every generation of the evolution, the wind power output (cost function) is computed through a recently developed and validated analytical wake model with a Gaussian profile velocity deficit [1], which has shown to outperform the traditionally employed wake models through different LES simulations and wind tunnel experiments. Two schemes with slightly different perimeter constraint conditions (full or partial) are tested. Results show, compared to the baseline, gridded layout, a wind power output increase between 5.5% and 7.7%. In addition, it is observed that the electric cable length at the facilities is reduced by up to 21%. [1] Bastankhah, Majid, and Fernando Porté-Agel. "A new analytical model for wind-turbine wakes." Renewable Energy 70 (2014): 116-123.

Tree-Structured Digital Organisms Model

NASA Astrophysics Data System (ADS)

Suzuki, Teruhiko; Nobesawa, Shiho; Tahara, Ikuo

Tierra and Avida are well-known models of digital organisms. They describe a life process as a sequence of computation codes. A linear sequence model may not be the only way to describe a digital organism, though it is very simple for a computer-based model. Thus we propose a new digital organism model based on a tree structure, which is rather similar to the generic programming. With our model, a life process is a combination of various functions, as if life in the real world is. This implies that our model can easily describe the hierarchical structure of life, and it can simulate evolutionary computation through mutual interaction of functions. We verified our model by simulations that our model can be regarded as a digital organism model according to its definitions. Our model even succeeded in creating species such as viruses and parasites.

When new human-modified habitats favour the expansion of an amphibian pioneer species: Evolutionary history of the natterjack toad (Bufo calamita) in a coal basin.

PubMed

Faucher, Leslie; Hénocq, Laura; Vanappelghem, Cédric; Rondel, Stéphanie; Quevillart, Robin; Gallina, Sophie; Godé, Cécile; Jaquiéry, Julie; Arnaud, Jean-François

2017-09-01

Human activities affect microevolutionary dynamics by inducing environmental changes. In particular, land cover conversion and loss of native habitats decrease genetic diversity and jeopardize the adaptive ability of populations. Nonetheless, new anthropogenic habitats can also promote the successful establishment of emblematic pioneer species. We investigated this issue by examining the population genetic features and evolutionary history of the natterjack toad (Bufo [Epidalea] calamita) in northern France, where populations can be found in native coastal habitats and coalfield habitats shaped by European industrial history, along with an additional set of European populations located outside this focal area. We predicted contrasting patterns of genetic structure, with newly settled coalfield populations departing from migration-drift equilibrium. As expected, coalfield populations showed a mosaic of genetically divergent populations with short-range patterns of gene flow, and native coastal populations indicated an equilibrium state with an isolation-by-distance pattern suggestive of postglacial range expansion. However, coalfield populations exhibited (i) high levels of genetic diversity, (ii) no evidence of local inbreeding or reduced effective population size and (iii) multiple maternal mitochondrial lineages, a genetic footprint depicting independent colonization events. Furthermore, approximate Bayesian computations suggested several evolutionary trajectories from ancient isolation in glacial refugia during the Pleistocene, with biogeographical signatures of recent expansion probably confounded by human-mediated mixing of different lineages. From an evolutionary and conservation perspective, this study highlights the ecological value of industrial areas, provided that ongoing regional gene flow is ensured within the existing lineage boundaries. © 2017 John Wiley & Sons Ltd.

Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes.

PubMed

Weaver, Steven; Shank, Stephen D; Spielman, Stephanie J; Li, Michael; Muse, Spencer V; Kosakovsky Pond, Sergei L

2018-01-02

Inference of how evolutionary forces have shaped extant genetic diversity is a cornerstone of modern comparative sequence analysis. Advances in sequence generation and increased statistical sophistication of relevant methods now allow researchers to extract ever more evolutionary signal from the data, albeit at an increased computational cost. Here, we announce the release of Datamonkey 2.0, a completely re-engineered version of the Datamonkey web-server for analyzing evolutionary signatures in sequence data. For this endeavor, we leveraged recent developments in open-source libraries that facilitate interactive, robust, and scalable web application development. Datamonkey 2.0 provides a carefully curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application. To complement Datamonkey 2.0, we additionally release HyPhy Vision, an accompanying JavaScript application for visualizing analysis results. HyPhy Vision can also be used separately from Datamonkey 2.0 to visualize locally-executed HyPhy analyses. Together, Datamonkey 2.0 and HyPhy Vision showcase how scientific software development can benefit from general-purpose open-source frameworks. Datamonkey 2.0 is freely and publicly available at http://www.datamonkey. org, and the underlying codebase is available from https://github.com/veg/datamonkey-js. © The Author 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Influence of Atomic Diffusion on Stellar Ages and Chemical Tagging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dotter, Aaron; Conroy, Charlie; Cargile, Phillip

2017-05-10

In the era of large stellar spectroscopic surveys, there is an emphasis on deriving not only stellar abundances but also the ages for millions of stars. In the context of Galactic archeology, stellar ages provide a direct probe of the formation history of the Galaxy. We use the stellar evolution code MESA to compute models with atomic diffusion—with and without radiative acceleration—and extra mixing in the surface layers. The extra mixing consists of both density-dependent turbulent mixing and envelope overshoot mixing. Based on these models we argue that it is important to distinguish between initial, bulk abundances (parameters) and current,more » surface abundances (variables) in the analysis of individual stellar ages. In stars that maintain radiative regions on evolutionary timescales, atomic diffusion modifies the surface abundances. We show that when initial, bulk metallicity is equated with current, surface metallicity in isochrone age analysis, the resulting stellar ages can be systematically overestimated by up to 20%. The change of surface abundances with evolutionary phase also complicates chemical tagging, which is the concept that dispersed star clusters can be identified through unique, high-dimensional chemical signatures. Stars from the same cluster, but in different evolutionary phases, will show different surface abundances. We speculate that calibration of stellar models may allow us to estimate not only stellar ages but also initial abundances for individual stars. In the meantime, analyzing the chemical properties of stars in similar evolutionary phases is essential to minimize the effects of atomic diffusion in the context of chemical tagging.« less

Inferring the mode of origin of polyploid species from next-generation sequence data.

PubMed

Roux, Camille; Pannell, John R

2015-03-01

Many eukaryote organisms are polyploid. However, despite their importance, evolutionary inference of polyploid origins and modes of inheritance has been limited by a need for analyses of allele segregation at multiple loci using crosses. The increasing availability of sequence data for nonmodel species now allows the application of established approaches for the analysis of genomic data in polyploids. Here, we ask whether approximate Bayesian computation (ABC), applied to realistic traditional and next-generation sequence data, allows correct inference of the evolutionary and demographic history of polyploids. Using simulations, we evaluate the robustness of evolutionary inference by ABC for tetraploid species as a function of the number of individuals and loci sampled, and the presence or absence of an outgroup. We find that ABC adequately retrieves the recent evolutionary history of polyploid species on the basis of both old and new sequencing technologies. The application of ABC to sequence data from diploid and polyploid species of the plant genus Capsella confirms its utility. Our analysis strongly supports an allopolyploid origin of C. bursa-pastoris about 80 000 years ago. This conclusion runs contrary to previous findings based on the same data set but using an alternative approach and is in agreement with recent findings based on whole-genome sequencing. Our results indicate that ABC is a promising and powerful method for revealing the evolution of polyploid species, without the need to attribute alleles to a homeologous chromosome pair. The approach can readily be extended to more complex scenarios involving higher ploidy levels. © 2015 John Wiley & Sons Ltd.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer

PubMed Central

Li, Lei; Tibiche, Chabane; Fu, Cong; Kaneko, Tomonori; Moran, Michael F.; Schiller, Martin R.; Li, Shawn Shun-Cheng; Wang, Edwin

2012-01-01

Phosphotyrosine (pTyr) signaling, which plays a central role in cell–cell and cell–environment interactions, has been considered to be an evolutionary innovation in multicellular metazoans. However, neither the emergence nor the evolution of the human pTyr signaling system is currently understood. Tyrosine kinase (TK) circuits, each of which consists of a TK writer, a kinase substrate, and a related reader, such as Src homology (SH) 2 domains and pTyr-binding (PTB) domains, comprise the core machinery of the pTyr signaling network. In this study, we analyzed the evolutionary trajectories of 583 literature-derived and 50,000 computationally predicted human TK circuits in 19 representative eukaryotic species and assigned their evolutionary origins. We found that human TK circuits for intracellular pTyr signaling originated largely from primitive organisms, whereas the inter- or extracellular signaling circuits experienced significant expansion in the bilaterian lineage through the “back-wiring” of newly evolved kinases to primitive substrates and SH2/PTB domains. Conversely, the TK circuits that are involved in tissue-specific signaling evolved mainly in vertebrates by the back-wiring of vertebrate substrates to primitive kinases and SH2/PTB domains. Importantly, we found that cancer signaling preferentially employs the pTyr sites, which are linked to more TK circuits. Our work provides insights into the evolutionary paths of the human pTyr signaling circuits and suggests the use of a network approach for cancer intervention through the targeting of key pTyr sites and their associated signaling hubs in the network. PMID:22194470

Ecological divergence and evolutionary transition of resprouting types in Banksia attenuata.

PubMed

He, Tianhua

2014-08-01

Resprouting is a key functional trait that allows plants to survive diverse disturbances. The fitness benefits associated with resprouting include a rapid return to adult growth, early flowering, and setting seed. The resprouting responses observed following fire are varied, as are the ecological outcomes. Understanding the ecological divergence and evolutionary pathways of different resprouting types and how the environment and genetics interact to drive such morphological evolution represents an important, but under-studied, topic. In the present study, microsatellite markers and microevolutionary approaches were used to better understand: (1) whether genetic differentiation is related to morphological divergence among resprouting types and if so, whether there are any specific genetic variations associated with morphological divergence and (2) the evolutionary pathway of the transitions between two resprouting types in Banksia attenuata (epicormic resprouting from aerial stems or branch; resprouting from a underground lignotuber). The results revealed an association between population genetic differentiation and the morphological divergence of postfire resprouting types in B. attenuata. A microsatellite allele has been shown to be associated with epicormic populations. Approximate Bayesian Computation analysis revealed a likely evolutionary transition from epicormic to lignotuberous resprouting in B. attenuata. It is concluded that the postfire resprouting type in B. attenuata is likely determined by the fire's characteristics. The differentiated expression of postfire resprouting types in different environments is likely a consequence of local genetic adaptation. The capacity to shift the postfire resprouting type to adapt to diverse fire regimes is most likely the key factor explaining why B. attenuata is the most widespread member of the Banksia genus.

A Systematic Bayesian Integration of Epidemiological and Genetic Data

PubMed Central

Lau, Max S. Y.; Marion, Glenn; Streftaris, George; Gibson, Gavin

2015-01-01

Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process. PMID:26599399