Artificial gravity Mars spaceship

NASA Technical Reports Server (NTRS)

Clark, Benton C.

1989-01-01

Experience gained in the study of artificial gravity for a manned trip to Mars is reviewed, and a snowflake-configured interplanetary vehicle cluster of habitat modules, descent vehicles, and propulsion systems is presented. An evolutionary design is described which permits sequential upgrading from five to nine crew members, an increase of landers from one to as many a three per mission, and an orderly, phased incorporation of advanced technologies as they become available.

Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope?

PubMed Central

Bartlett, Roger

2006-01-01

This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements (‘techniques’) and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key Points Expert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis. Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear. Other AI applications, including Evolutionary Computation, have received little attention. PMID:24357939

Artificial intelligence in sports biomechanics: new dawn or false hope?

PubMed

Bartlett, Roger

2006-12-15

This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques') and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key PointsExpert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis.Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear.Other AI applications, including Evolutionary Computation, have received little attention.

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

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.

Darwin, artificial selection, and poverty.

PubMed

Sanchez, Luis

2010-03-01

This paper argues that the processes of evolutionary selection are becoming increasingly artificial, a trend that goes against the belief in a purely natural selection process claimed by Darwin's natural selection theory. Artificial selection is mentioned by Darwin, but it was ignored by Social Darwinists, and it is all but absent in neo-Darwinian thinking. This omission results in an underestimation of probable impacts of artificial selection upon assumed evolutionary processes, and has implications for the ideological uses of Darwin's language, particularly in relation to poverty and other social inequalities. The influence of artificial selection on genotypic and phenotypic adaptations arguably represents a substantial shift in the presumed path of evolution, a shift laden with both biological and political implications.

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.

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

Brazilian and Mexican experiences in the study of incipient domestication.

PubMed

Lins Neto, Ernani Machado de Freitas; Peroni, Nivaldo; Casas, Alejandro; Parra, Fabiola; Aguirre, Xitlali; Guillén, Susana; Albuquerque, Ulysses Paulino

2014-04-02

Studies of domestication enables a better understanding of human cultures, landscape changes according to peoples' purposes, and evolutionary consequences of human actions on biodiversity. This review aimed at discussing concepts, hypotheses, and current trends in studies of domestication of plants, using examples of cases studied in regions of Mesoamerica and Brazil. We analyzed trends of ethnobiological studies contributing to document processes of domestication and to establish criteria for biodiversity conservation based on traditional ecological knowledge. Based on reviewing our own and other authors' studies we analyzed management patterns and evolutionary trends associated to domestication occurring at plant populations and landscape levels. Particularly, we systematized information documenting: ethnobotanical aspects about plant management and artificial selection mechanisms, morphological consequences of plant management, population genetics of wild and managed plant populations, trends of change in reproduction systems of plants associated to management, and other ecological and physiological aspects influenced by management and domestication. Based on the analysis of study cases of 20 native species of herbs, shrubs and trees we identified similar criteria of artificial selection in different cultural contexts of Mexico and Brazil. Similar evolutionary trends were also identified in morphology (selection in favor of gigantism of useful and correlated parts); organoleptic characteristics such as taste, toxicity, color, texture; reproductive biology, mainly breeding system, phenological changes, and population genetics aspects, maintenance or increasing of genetic diversity in managed populations, high gene flow with wild relatives and low structure maintained by artificial selection. Our review is a first attempt to unify research methods for analyzing a high diversity of processes. Further research should emphasize deeper analyses of contrasting and diverse cultural and ecological contexts for a better understanding of evolution under incipient processes of domestication. Higher research effort is particularly required in Brazil, where studies on this topic are scarcer than in Mexico but where diversity of human cultures managing their also high plant resources diversity offer high potential for documenting the diversity of mechanisms of artificial selection and evolutionary trends. Comparisons and evaluations of incipient domestication in the regions studied as well as the Andean area would significantly contribute to understanding origins and diffusion of the experience of managing and domesticating plants.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Hybrid Motion Planning with Multiple Destinations

NASA Technical Reports Server (NTRS)

Clouse, Jeffery

1998-01-01

In our initial proposal, we laid plans for developing a hybrid motion planning system that combines the concepts of visibility-based motion planning, artificial potential field based motion planning, evolutionary constrained optimization, and reinforcement learning. Our goal was, and still is, to produce a hybrid motion planning system that outperforms the best traditional motion planning systems on problems with dynamic environments. The proposed hybrid system will be in two parts the first is a global motion planning system and the second is a local motion planning system. The global system will take global information about the environment, such as the placement of the obstacles and goals, and produce feasible paths through those obstacles. We envision a system that combines the evolutionary-based optimization and visibility-based motion planning to achieve this end.

Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection.

PubMed

Edward, Dominic A; Fricke, Claudia; Chapman, Tracey

2010-08-27

Artificial selection and experimental evolution document natural selection under controlled conditions. Collectively, these techniques are continuing to provide fresh and important insights into the genetic basis of evolutionary change, and are now being employed to investigate mating behaviour. Here, we focus on how selection techniques can reveal the genetic basis of post-mating adaptations to sexual selection and sexual conflict. Alteration of the operational sex ratio of adult Drosophila over just a few tens of generations can lead to altered ejaculate allocation patterns and the evolution of resistance in females to the costly effects of elevated mating rates. We provide new data to show how male responses to the presence of rivals can evolve. For several traits, the way in which males responded to rivals was opposite in lines selected for male-biased, as opposed to female-biased, adult sex ratio. This shows that the manipulation of the relative intensity of intra- and inter-sexual selection can lead to replicable and repeatable effects on mating systems, and reveals the potential for significant contemporary evolutionary change. Such studies, with important safeguards, have potential utility for understanding sexual selection and sexual conflict across many taxa. We discuss how artificial selection studies combined with genomics will continue to deepen our knowledge of the evolutionary principles first laid down by Darwin 150 years ago.

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

PubMed Central

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise. PMID:22187523

Robust design of biological circuits: evolutionary systems biology approach.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

Brazilian and Mexican experiences in the study of incipient domestication

PubMed Central

2014-01-01

Background Studies of domestication enables a better understanding of human cultures, landscape changes according to peoples’ purposes, and evolutionary consequences of human actions on biodiversity. This review aimed at discussing concepts, hypotheses, and current trends in studies of domestication of plants, using examples of cases studied in regions of Mesoamerica and Brazil. We analyzed trends of ethnobiological studies contributing to document processes of domestication and to establish criteria for biodiversity conservation based on traditional ecological knowledge. Methods Based on reviewing our own and other authors’ studies we analyzed management patterns and evolutionary trends associated to domestication occurring at plant populations and landscape levels. Particularly, we systematized information documenting: ethnobotanical aspects about plant management and artificial selection mechanisms, morphological consequences of plant management, population genetics of wild and managed plant populations, trends of change in reproduction systems of plants associated to management, and other ecological and physiological aspects influenced by management and domestication. Results Based on the analysis of study cases of 20 native species of herbs, shrubs and trees we identified similar criteria of artificial selection in different cultural contexts of Mexico and Brazil. Similar evolutionary trends were also identified in morphology (selection in favor of gigantism of useful and correlated parts); organoleptic characteristics such as taste, toxicity, color, texture; reproductive biology, mainly breeding system, phenological changes, and population genetics aspects, maintenance or increasing of genetic diversity in managed populations, high gene flow with wild relatives and low structure maintained by artificial selection. Our review is a first attempt to unify research methods for analyzing a high diversity of processes. Further research should emphasize deeper analyses of contrasting and diverse cultural and ecological contexts for a better understanding of evolution under incipient processes of domestication. Conclusion Higher research effort is particularly required in Brazil, where studies on this topic are scarcer than in Mexico but where diversity of human cultures managing their also high plant resources diversity offer high potential for documenting the diversity of mechanisms of artificial selection and evolutionary trends. Comparisons and evaluations of incipient domestication in the regions studied as well as the Andean area would significantly contribute to understanding origins and diffusion of the experience of managing and domesticating plants. PMID:24694009

Artificial selection on ant female caste ratio uncovers a link between female-biased sex ratios and infection by Wolbachia endosymbionts.

PubMed

Pontieri, L; Schmidt, A M; Singh, R; Pedersen, J S; Linksvayer, T A

2017-02-01

Social insect sex and caste ratios are well-studied targets of evolutionary conflicts, but the heritable factors affecting these traits remain unknown. To elucidate these factors, we carried out a short-term artificial selection study on female caste ratio in the ant Monomorium pharaonis. Across three generations of bidirectional selection, we observed no response for caste ratio, but sex ratios rapidly became more female-biased in the two replicate high selection lines and less female-biased in the two replicate low selection lines. We hypothesized that this rapid divergence for sex ratio was caused by changes in the frequency of infection by the heritable bacterial endosymbiont Wolbachia, because the initial breeding stock varied for Wolbachia infection, and Wolbachia is known to cause female-biased sex ratios in other insects. Consistent with this hypothesis, the proportions of Wolbachia-infected colonies in the selection lines changed rapidly, mirroring the sex ratio changes. Moreover, the estimated effect of Wolbachia on sex ratio (~13% female bias) was similar in colonies before and during artificial selection, indicating that this Wolbachia effect is likely independent of the effects of artificial selection on other heritable factors. Our study provides evidence for the first case of endosymbiont sex ratio manipulation in a social insect. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Evolutionary effects of alternative artificial propagation programs: implications for viability of endangered anadromous salmonids

PubMed Central

McClure, Michelle M; Utter, Fred M; Baldwin, Casey; Carmichael, Richard W; Hassemer, Peter F; Howell, Philip J; Spruell, Paul; Cooney, Thomas D; Schaller, Howard A; Petrosky, Charles E

2008-01-01

Most hatchery programs for anadromous salmonids have been initiated to increase the numbers of fish for harvest, to mitigate for habitat losses, or to increase abundance in populations at low abundance. However, the manner in which these programs are implemented can have significant impacts on the evolutionary trajectory and long-term viability of populations. In this paper, we review the potential benefits and risks of hatchery programs relative to the conservation of species listed under the US Endangered Species Act. To illustrate, we present the range of potential effects within a population as well as among populations of Chinook salmon (Oncorhynchus tshawytscha) where changes to major hatchery programs are being considered. We apply evolutionary considerations emerging from these examples to suggest broader principles for hatchery uses that are consistent with conservation goals. We conclude that because of the evolutionary risks posed by artificial propagation programs, they should not be viewed as a substitute for addressing other limiting factors that prevent achieving viability. At the population level, artificial propagation programs that are implemented as a short-term approach to avoid imminent extinction are more likely to achieve long-term population viability than approaches that rely on long-term supplementation. In addition, artificial propagation programs can have out-of-population impacts that should be considered in conservation planning. PMID:25567637

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem.

PubMed

Zamdborg, Leonid; Holloway, David M; Merelo, Juan J; Levchenko, Vladimir F; Spirov, Alexander V

2015-06-10

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of "genomic parasites", such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem

PubMed Central

Zamdborg, Leonid; Holloway, David M.; Merelo, Juan J.; Levchenko, Vladimir F.; Spirov, Alexander V.

2015-01-01

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of “genomic parasites”, such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts. PMID:25767296

Optimal Solution for an Engineering Applications Using Modified Artificial Immune System

NASA Astrophysics Data System (ADS)

Padmanabhan, S.; Chandrasekaran, M.; Ganesan, S.; patan, Mahamed Naveed Khan; Navakanth, Polina

2017-03-01

An Engineering optimization leads a essential role in several engineering application areas like process design, product design, re-engineering and new product development, etc. In engineering, an awfully best answer is achieved by comparison to some completely different solutions by utilization previous downside information. An optimization algorithms provide systematic associate degreed economical ways that within which of constructing and comparison new design solutions so on understand at best vogue, thus on best solution efficiency and acquire the foremost wonderful design impact. In this paper, a new evolutionary based Modified Artificial Immune System (MAIS) algorithm used to optimize an engineering application of gear drive design. The results are compared with existing design.

Banteng and Bali cattle in Indonesia: status and forecasts.

PubMed

Purwantara, B; Noor, R R; Andersson, G; Rodriguez-Martinez, H

2012-01-01

Bali cattle still represents 27% of the total cattle population in Indonesia, and it is considered the pillar breed for small farmers. Moreover, it is a breed of evolutionary importance regarding its direct ancestry from Banteng. However, there is a need for the establishment of a rational system for the evaluation of breeding soundness for indigenous Bali bulls to be used as sires for artificial insemination breeding programmes. Moreover, there is a need for cryobanking of well-identified genetic resources pertaining their use in evolutionary research and application as essential germplasm in breeding programmes. © 2012 Blackwell Verlag GmbH.

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

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

Product Mix Selection Using AN Evolutionary Technique

NASA Astrophysics Data System (ADS)

Tsoulos, Ioannis G.; Vasant, Pandian

2009-08-01

This paper proposes an evolutionary technique for the solution of a real—life industrial problem and particular for the product mix selection problem. The evolutionary technique is a combination of a genetic algorithm that preserves the feasibility of the trial solutions with penalties and some local optimization method. The goal of this paper has been achieved in finding the best near optimal solution for the profit fitness function respect to vagueness factor and level of satisfaction. The findings of the profit values will be very useful for the decision makers in the industrial engineering sector for the implementation purpose. It's possible to improve the solutions obtained in this study by employing other meta-heuristic methods such as simulated annealing, tabu Search, ant colony optimization, particle swarm optimization and artificial immune systems.

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].

Practical advantages of evolutionary computation

NASA Astrophysics Data System (ADS)

Fogel, David B.

1997-10-01

Evolutionary computation is becoming a common technique for solving difficult, real-world problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as their ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine.

Artificial evolution by viability rather than competition.

PubMed

Maesani, Andrea; Fernando, Pradeep Ruben; Floreano, Dario

2014-01-01

Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.

Analysis of Environmental Stress Factors Using an Artificial Growth System and Plant Fitness Optimization

PubMed Central

Lee, Meonghun; Yoe, Hyun

2015-01-01

The environment promotes evolution. Evolutionary processes represent environmental adaptations over long time scales; evolution of crop genomes is not inducible within the relatively short time span of a human generation. Extreme environmental conditions can accelerate evolution, but such conditions are often stress inducing and disruptive. Artificial growth systems can be used to induce and select genomic variation by changing external environmental conditions, thus, accelerating evolution. By using cloud computing and big-data analysis, we analyzed environmental stress factors for Pleurotus ostreatus by assessing, evaluating, and predicting information of the growth environment. Through the indexing of environmental stress, the growth environment can be precisely controlled and developed into a technology for improving crop quality and production. PMID:25874206

Stochastic nonlinear dynamics pattern formation and growth models

PubMed Central

Yaroslavsky, Leonid P

2007-01-01

Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature. PMID:17908341

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

PubMed

Hashim, H A; Abido, M A

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

PubMed Central

Hashim, H. A.; Abido, M. A.

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Elements of decisional dynamics: An agent-based approach applied to artificial financial market.

PubMed

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background.

PubMed

Penco, Silvana; Buscema, Massimo; Patrosso, Maria Cristina; Marocchi, Alessandro; Grossi, Enzo

2008-05-30

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg. This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.

Engineering the evolution of self-organizing behaviors in swarm robotics: a case study.

PubMed

Trianni, Vito; Nolfi, Stefano

2011-01-01

Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Application of artificial intelligence in Geodesy - A review of theoretical foundations and practical examples

NASA Astrophysics Data System (ADS)

Reiterer, Alexander; Egly, Uwe; Vicovac, Tanja; Mai, Enrico; Moafipoor, Shahram; Grejner-Brzezinska, Dorota A.; Toth, Charles K.

2010-12-01

Artificial Intelligence (AI) is one of the key technologies in many of today's novel applications. It is used to add knowledge and reasoning to systems. This paper illustrates a review of AI methods including examples of their practical application in Geodesy like data analysis, deformation analysis, navigation, network adjustment, and optimization of complex measurement procedures. We focus on three examples, namely, a geo-risk assessment system supported by a knowledge-base, an intelligent dead reckoning personal navigator, and evolutionary strategies for the determination of Earth gravity field parameters. Some of the authors are members of IAG Sub-Commission 4.2 - Working Group 4.2.3, which has the main goal to study and report on the application of AI in Engineering Geodesy.

The painted turtle, Chrysemys picta: a model system for vertebrate evolution, ecology, and human health.

PubMed

Valenzuela, Nicole

2009-07-01

Painted turtles (Chrysemys picta) are representatives of a vertebrate clade whose biology and phylogenetic position hold a key to our understanding of fundamental aspects of vertebrate evolution. These features make them an ideal emerging model system. Extensive ecological and physiological research provide the context in which to place new research advances in evolutionary genetics, genomics, evolutionary developmental biology, and ecological developmental biology which are enabled by current resources, such as a bacterial artificial chromosome (BAC) library of C. picta, and the imminent development of additional ones such as genome sequences and cDNA and expressed sequence tag (EST) libraries. This integrative approach will allow the research community to continue making advances to provide functional and evolutionary explanations for the lability of biological traits found not only among reptiles but vertebrates in general. Moreover, because humans and reptiles share a common ancestor, and given the ease of using nonplacental vertebrates in experimental biology compared with mammalian embryos, painted turtles are also an emerging model system for biomedical research. For example, painted turtles have been studied to understand many biological responses to overwintering and anoxia, as potential sentinels for environmental xenobiotics, and as a model to decipher the ecology and evolution of sexual development and reproduction. Thus, painted turtles are an excellent reptilian model system for studies with human health, environmental, ecological, and evolutionary significance.

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Insights into resource consumption, cross-feeding, system collapse, stability and biodiversity from an artificial ecosystem

PubMed Central

Sumpter, David

2017-01-01

Community ecosystems at very different levels of biological organization often have similar properties. Coexistence of multiple species, cross-feeding, biodiversity and fluctuating population dynamics are just a few of the properties that arise in a range of ecological settings. Here we develop a bottom-up model of consumer–resource interactions, in the form of an artificial ecosystem ‘number soup’, which reflects basic properties of many bacterial and other community ecologies. We demonstrate four key properties of the number soup model: (i) communities self-organize so that all available resources are fully consumed; (ii) reciprocal cross-feeding is a common evolutionary outcome, which evolves in a number of stages, and many transitional species are involved; (iii) the evolved ecosystems are often ‘robust yet fragile’, with keystone species required to prevent the whole system from collapsing; (iv) non-equilibrium dynamics and chaotic patterns are general properties, readily generating rich biodiversity. These properties have been observed in empirical ecosystems, ranging from bacteria to rainforests. Establishing similar properties in an evolutionary model as simple as the number soup suggests that these four properties are ubiquitous features of all community ecosystems, and raises questions about how we interpret ecosystem structure in the context of natural selection. PMID:28100827

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.

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.

Exploring the effect of power law social popularity on language evolution.

PubMed

Gong, Tao; Shuai, Lan

2014-01-01

We evaluate the effect of a power-law-distributed social popularity on the origin and change of language, based on three artificial life models meticulously tracing the evolution of linguistic conventions including lexical items, categories, and simple syntax. A cross-model analysis reveals an optimal social popularity, in which the λ value of the power law distribution is around 1.0. Under this scaling, linguistic conventions can efficiently emerge and widely diffuse among individuals, thus maintaining a useful level of mutual understandability even in a big population. From an evolutionary perspective, we regard this social optimality as a tradeoff among social scaling, mutual understandability, and population growth. Empirical evidence confirms that such optimal power laws exist in many large-scale social systems that are constructed primarily via language-related interactions. This study contributes to the empirical explorations and theoretical discussions of the evolutionary relations between ubiquitous power laws in social systems and relevant individual behaviors.

Gene drive systems for insect disease vectors.

PubMed

Sinkins, Steven P; Gould, Fred

2006-06-01

The elegant mechanisms by which naturally occurring selfish genetic elements, such as transposable elements, meiotic drive genes, homing endonuclease genes and Wolbachia, spread at the expense of their hosts provide some of the most fascinating and remarkable subjects in evolutionary genetics. These elements also have enormous untapped potential to be used in the control of some of the world's most devastating diseases. Effective gene drive systems for spreading genes that can block the transmission of insect-borne pathogens are much needed. Here we explore the potential of natural gene drive systems and discuss the artificial constructs that could be envisaged for this purpose.

Evolutionary domestication in Drosophila subobscura.

PubMed

Simões, P; Rose, M R; Duarte, A; Gonçalves, R; Matos, M

2007-03-01

The domestication of plants and animals is historically one of the most important topics in evolutionary biology. The evolutionary genetic changes arising from human cultivation are complex because of the effects of such varied processes as continuing natural selection, artificial selection, deliberate inbreeding, genetic drift and hybridization of different lineages. Despite the interest of domestication as an evolutionary process, few studies of multicellular sexual species have approached this topic using well-replicated experiments. Here we present a comprehensive study in which replicated evolutionary trajectories from several Drosophila subobscura populations provide a detailed view of the evolutionary dynamics of domestication in an outbreeding animal species. Our results show a clear evolutionary response in fecundity traits, but no clear pattern for adult starvation resistance and juvenile traits such as development time and viability. These results supply new perspectives on the confounding of adaptation with other evolutionary mechanisms in the process of domestication.

Evolutionary combinatorial chemistry, a novel tool for SAR studies on peptide transport across the blood-brain barrier. Part 2. Design, synthesis and evaluation of a first generation of peptides.

PubMed

Teixidó, Meritxell; Belda, Ignasi; Zurita, Esther; Llorà, Xavier; Fabre, Myriam; Vilaró, Senén; Albericio, Fernando; Giralt, Ernest

2005-12-01

The use of high-throughput methods in drug discovery allows the generation and testing of a large number of compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA). Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood-brain barrier (BBB). This paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the blood-brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N-methyl amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay, log Poctanol/water calculations, etc. Finally, a second generation has been proposed. (c) 2005 European Peptide Society and John Wiley & Sons, Ltd.

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

PubMed Central

Tonelli, Paul; Mouret, Jean-Baptiste

2013-01-01

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities. PMID:24236099

Dual Rationality and Deliberative Agents

NASA Astrophysics Data System (ADS)

Debenham, John; Sierra, Carles

Human agents deliberate using models based on reason for only a minute proportion of the decisions that they make. In stark contrast, the deliberation of artificial agents is heavily dominated by formal models based on reason such as game theory, decision theory and logic—despite that fact that formal reasoning will not necessarily lead to superior real-world decisions. Further the Nobel Laureate Friedrich Hayek warns us of the ‘fatal conceit’ in controlling deliberative systems using models based on reason as the particular model chosen will then shape the system’s future and either impede, or eventually destroy, the subtle evolutionary processes that are an integral part of human systems and institutions, and are crucial to their evolution and long-term survival. We describe an architecture for artificial agents that is founded on Hayek’s two rationalities and supports the two forms of deliberation used by mankind.

Comparison of some evolutionary algorithms for optimization of the path synthesis problem

NASA Astrophysics Data System (ADS)

Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

2018-01-01

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

Multigenerational response to artificial selection for biased clutch sex ratios in Tigriopus californicus populations.

PubMed

Alexander, H J; Richardson, J M L; Anholt, B R

2014-09-01

Polygenic sex determination (PSD) is relatively rare and theoretically evolutionary unstable, yet has been reported across a range of taxa. Evidence for multilocus PSD is provided by (i) large between-family variance in sex ratio, (ii) paternal and maternal effects on family sex ratio and (iii) response to selection for family sex ratio. This study tests the polygenic hypothesis of sex determination in the harpacticoid copepod Tigriopus californicus using the criterion of response to selection. We report the first multigenerational quantitative evidence that clutch sex ratio responds to artificial selection in both directions (selection for male- and female-biased families) and in multiple populations of T. californicus. In the five of six lines that showed a response to selection, realized heritability estimated by multigenerational analysis ranged from 0.24 to 0.58. Divergence of clutch sex ratio between selection lines is rapid, with response to selection detectable within the first four generations of selection. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Information in the Biosphere: Biological and Digital Worlds.

PubMed

Gillings, Michael R; Hilbert, Martin; Kemp, Darrell J

2016-03-01

Evolution has transformed life through key innovations in information storage and replication, including RNA, DNA, multicellularity, and culture and language. We argue that the carbon-based biosphere has generated a cognitive system (humans) capable of creating technology that will result in a comparable evolutionary transition. Digital information has reached a similar magnitude to information in the biosphere. It increases exponentially, exhibits high-fidelity replication, evolves through differential fitness, is expressed through artificial intelligence (AI), and has facility for virtually limitless recombination. Like previous evolutionary transitions, the potential symbiosis between biological and digital information will reach a critical point where these codes could compete via natural selection. Alternatively, this fusion could create a higher-level superorganism employing a low-conflict division of labor in performing informational tasks. Copyright © 2015 Elsevier Ltd. All rights reserved.

High-Level Connectionist Models

DTIC Science & Technology

1993-04-01

The Ohio State University, Columbus Ohio. To appearto Artifcial Life IlL Angeline, P., Saunders, G., Pollack, J. (1993). An evolutionary algorithm...of Robotics and Automation, 2(1):14-23. Brooks, R. A. (1991). Intelligence without representations. Artificial Intelligence , 47:139- 159. Connell, J. H...1990). Minimalist Mobile Robotics: A Colony-style Architecture for an Creature, Volume 5 of Perspectives in Artificial Intelligence . Academic Press

A hybrid neural learning algorithm using evolutionary learning and derivative free local search method.

PubMed

Ghosh, Ranadhir; Yearwood, John; Ghosh, Moumita; Bagirov, Adil

2006-06-01

In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

PubMed

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

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.

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

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.

Information-theoretic decomposition of embodied and situated systems.

PubMed

Da Rold, Federico

2018-07-01

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

An information entropy model on clinical assessment of patients based on the holographic field of meridian

NASA Astrophysics Data System (ADS)

Wu, Jingjing; Wu, Xinming; Li, Pengfei; Li, Nan; Mao, Xiaomei; Chai, Lihe

2017-04-01

Meridian system is not only the basis of traditional Chinese medicine (TCM) method (e.g. acupuncture, massage), but also the core of TCM's basic theory. This paper has introduced a new informational perspective to understand the reality and the holographic field of meridian. Based on maximum information entropy principle (MIEP), a dynamic equation for the holographic field has been deduced, which reflects the evolutionary characteristics of meridian. By using self-organizing artificial neural network as algorithm, the evolutionary dynamic equation of the holographic field can be resolved to assess properties of meridians and clinically diagnose the health characteristics of patients. Finally, through some cases from clinical patients (e.g. a 30-year-old male patient, an apoplectic patient, an epilepsy patient), we use this model to assess the evolutionary properties of meridians. It is proved that this model not only has significant implications in revealing the essence of meridian in TCM, but also may play a guiding role in clinical assessment of patients based on the holographic field of meridians.

Multi Agent Systems with Symbiotic Learning and Evolution using GNP

NASA Astrophysics Data System (ADS)

Eguchi, Toru; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

Evolvable mathematical models: A new artificial Intelligence paradigm

NASA Astrophysics Data System (ADS)

Grouchy, Paul

We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

Physiology of man and animals in the Tenth Five-Year Plan: Proceedings of the Thirteenth Congress of the I. P. Pavlov All-Union Physiological Society

NASA Technical Reports Server (NTRS)

Lange, K. A.

1980-01-01

Research in the field of animal and human physiology is reviewed. The following topics on problems of physiological science and related fields of knowledge are discussed: neurophysiology and higher nervous activity, physiology of sensory systems, physiology of visceral systems, evolutionary and ecological physiology, physiological cybernetics, computer application in physiology, information support of physiological research, history and theory of development of physiology. Also discussed were: artificial intelligence, physiological problems of reflex therapy, correlation of structure and function of the brain, adaptation and activity, microcirculation, and physiological studies in nerve and mental diseases.

Space station automation: the role of robotics and artificial intelligence (Invited Paper)

NASA Astrophysics Data System (ADS)

Park, W. T.; Firschein, O.

1985-12-01

Automation of the space station is necessary to make more effective use of the crew, to carry out repairs that are impractical or dangerous, and to monitor and control the many space station subsystems. Intelligent robotics and expert systems play a strong role in automation, and both disciplines are highly dependent on a common artificial intelligence (Al) technology base. The AI technology base provides the reasoning and planning capabilities needed in robotic tasks, such as perception of the environment and planning a path to a goal, and in expert systems tasks, such as control of subsystems and maintenance of equipment. This paper describes automation concepts for the space station, the specific robotic and expert systems required to attain this automation, and the research and development required. It also presents an evolutionary development plan that leads to fully automatic mobile robots for servicing satellites. Finally, we indicate the sequence of demonstrations and the research and development needed to confirm the automation capabilities. We emphasize that advanced robotics requires AI, and that to advance, AI needs the "real-world" problems provided by robotics.

ANUBIS: artificial neuromodulation using a Bayesian inference system.

PubMed

Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

2013-01-01

Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.

Natural selection stops the evolution of male attractiveness

PubMed Central

Hine, Emma; McGuigan, Katrina; Blows, Mark W.

2011-01-01

Sexual selection in natural populations acts on highly heritable traits and tends to be relatively strong, implicating sexual selection as a causal agent in many phenotypic radiations. Sexual selection appears to be ineffectual in promoting phenotypic divergence among contemporary natural populations, however, and there is little evidence from artificial selection experiments that sexual fitness can evolve. Here, we demonstrate that a multivariate male trait preferred by Drosophila serrata females can respond to selection and results in the maintenance of male mating success. The response to selection was associated with a gene of major effect increasing in frequency from 12 to 35% in seven generations. No further response to selection, or increase in frequency of the major gene, was observed between generations 7 and 11, indicating an evolutionary limit had been reached. Genetic analyses excluded both depletion of genetic variation and overdominance as causes of the evolutionary limit. Relaxing artificial selection resulted in the loss of 52% of the selection response after a further five generations, demonstrating that the response under artificial sexual selection was opposed by antagonistic natural selection. We conclude that male D. serrata sexually selected traits, and attractiveness to D. serrata females conferred by these traits, were held at an evolutionary limit by the lack of genetic variation that would allow an increase in sexual fitness while simultaneously maintaining nonsexual fitness. Our results suggest that sexual selection is unlikely to cause divergence among natural populations without a concomitant change in natural selection, a conclusion consistent with observational evidence from natural populations. PMID:21321197

Archaeogenomic insights into the adaptation of plants to the human environment: pushing plant-hominin co-evolution back to the Pliocene.

PubMed

Allaby, Robin G; Kistler, Logan; Gutaker, Rafal M; Ware, Roselyn; Kitchen, James L; Smith, Oliver; Clarke, Andrew C

2015-02-01

The colonization of the human environment by plants, and the consequent evolution of domesticated forms is increasingly being viewed as a co-evolutionary plant-human process that occurred over a long time period, with evidence for the co-evolutionary relationship between plants and humans reaching ever deeper into the hominin past. This developing view is characterized by a change in emphasis on the drivers of evolution in the case of plants. Rather than individual species being passive recipients of artificial selection pressures and ultimately becoming domesticates, entire plant communities adapted to the human environment. This evolutionary scenario leads to systems level genetic expectations from models that can be explored through ancient DNA and Next Generation Sequencing approaches. Emerging evidence suggests that domesticated genomes fit well with these expectations, with periods of stable complex evolution characterized by large amounts of change associated with relatively small selective value, punctuated by periods in which changes in one-half of the plant-hominin relationship cause rapid, low-complexity adaptation in the other. A corollary of a single plant-hominin co-evolutionary process is that clues about the initiation of the domestication process may well lie deep within the hominin lineage. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

The genome of the sea urchin Strongylocentrotus purpuratus.

PubMed

Sodergren, Erica; Weinstock, George M; Davidson, Eric H; Cameron, R Andrew; Gibbs, Richard A; Angerer, Robert C; Angerer, Lynne M; Arnone, Maria Ina; Burgess, David R; Burke, Robert D; Coffman, James A; Dean, Michael; Elphick, Maurice R; Ettensohn, Charles A; Foltz, Kathy R; Hamdoun, Amro; Hynes, Richard O; Klein, William H; Marzluff, William; McClay, David R; Morris, Robert L; Mushegian, Arcady; Rast, Jonathan P; Smith, L Courtney; Thorndyke, Michael C; Vacquier, Victor D; Wessel, Gary M; Wray, Greg; Zhang, Lan; Elsik, Christine G; Ermolaeva, Olga; Hlavina, Wratko; Hofmann, Gretchen; Kitts, Paul; Landrum, Melissa J; Mackey, Aaron J; Maglott, Donna; Panopoulou, Georgia; Poustka, Albert J; Pruitt, Kim; Sapojnikov, Victor; Song, Xingzhi; Souvorov, Alexandre; Solovyev, Victor; Wei, Zheng; Whittaker, Charles A; Worley, Kim; Durbin, K James; Shen, Yufeng; Fedrigo, Olivier; Garfield, David; Haygood, Ralph; Primus, Alexander; Satija, Rahul; Severson, Tonya; Gonzalez-Garay, Manuel L; Jackson, Andrew R; Milosavljevic, Aleksandar; Tong, Mark; Killian, Christopher E; Livingston, Brian T; Wilt, Fred H; Adams, Nikki; Bellé, Robert; Carbonneau, Seth; Cheung, Rocky; Cormier, Patrick; Cosson, Bertrand; Croce, Jenifer; Fernandez-Guerra, Antonio; Genevière, Anne-Marie; Goel, Manisha; Kelkar, Hemant; Morales, Julia; Mulner-Lorillon, Odile; Robertson, Anthony J; Goldstone, Jared V; Cole, Bryan; Epel, David; Gold, Bert; Hahn, Mark E; Howard-Ashby, Meredith; Scally, Mark; Stegeman, John J; Allgood, Erin L; Cool, Jonah; Judkins, Kyle M; McCafferty, Shawn S; Musante, Ashlan M; Obar, Robert A; Rawson, Amanda P; Rossetti, Blair J; Gibbons, Ian R; Hoffman, Matthew P; Leone, Andrew; Istrail, Sorin; Materna, Stefan C; Samanta, Manoj P; Stolc, Viktor; Tongprasit, Waraporn; Tu, Qiang; Bergeron, Karl-Frederik; Brandhorst, Bruce P; Whittle, James; Berney, Kevin; Bottjer, David J; Calestani, Cristina; Peterson, Kevin; Chow, Elly; Yuan, Qiu Autumn; Elhaik, Eran; Graur, Dan; Reese, Justin T; Bosdet, Ian; Heesun, Shin; Marra, Marco A; Schein, Jacqueline; Anderson, Michele K; Brockton, Virginia; Buckley, Katherine M; Cohen, Avis H; Fugmann, Sebastian D; Hibino, Taku; Loza-Coll, Mariano; Majeske, Audrey J; Messier, Cynthia; Nair, Sham V; Pancer, Zeev; Terwilliger, David P; Agca, Cavit; Arboleda, Enrique; Chen, Nansheng; Churcher, Allison M; Hallböök, F; Humphrey, Glen W; Idris, Mohammed M; Kiyama, Takae; Liang, Shuguang; Mellott, Dan; Mu, Xiuqian; Murray, Greg; Olinski, Robert P; Raible, Florian; Rowe, Matthew; Taylor, John S; Tessmar-Raible, Kristin; Wang, D; Wilson, Karen H; Yaguchi, Shunsuke; Gaasterland, Terry; Galindo, Blanca E; Gunaratne, Herath J; Juliano, Celina; Kinukawa, Masashi; Moy, Gary W; Neill, Anna T; Nomura, Mamoru; Raisch, Michael; Reade, Anna; Roux, Michelle M; Song, Jia L; Su, Yi-Hsien; Townley, Ian K; Voronina, Ekaterina; Wong, Julian L; Amore, Gabriele; Branno, Margherita; Brown, Euan R; Cavalieri, Vincenzo; Duboc, Véronique; Duloquin, Louise; Flytzanis, Constantin; Gache, Christian; Lapraz, François; Lepage, Thierry; Locascio, Annamaria; Martinez, Pedro; Matassi, Giorgio; Matranga, Valeria; Range, Ryan; Rizzo, Francesca; Röttinger, Eric; Beane, Wendy; Bradham, Cynthia; Byrum, Christine; Glenn, Tom; Hussain, Sofia; Manning, Gerard; Miranda, Esther; Thomason, Rebecca; Walton, Katherine; Wikramanayke, Athula; Wu, Shu-Yu; Xu, Ronghui; Brown, C Titus; Chen, Lili; Gray, Rachel F; Lee, Pei Yun; Nam, Jongmin; Oliveri, Paola; Smith, Joel; Muzny, Donna; Bell, Stephanie; Chacko, Joseph; Cree, Andrew; Curry, Stacey; Davis, Clay; Dinh, Huyen; Dugan-Rocha, Shannon; Fowler, Jerry; Gill, Rachel; Hamilton, Cerrissa; Hernandez, Judith; Hines, Sandra; Hume, Jennifer; Jackson, Laronda; Jolivet, Angela; Kovar, Christie; Lee, Sandra; Lewis, Lora; Miner, George; Morgan, Margaret; Nazareth, Lynne V; Okwuonu, Geoffrey; Parker, David; Pu, Ling-Ling; Thorn, Rachel; Wright, Rita

2006-11-10

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus, a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

The evolution of colour polymorphism in British winter-active Lepidoptera in response to search image use by avian predators.

PubMed

Weir, Jamie C

2018-05-10

Phenotypic polymorphism in cryptic species is widespread. This may evolve in response to search image use by predators exerting negative frequency-dependent selection on intraspecific colour morphs, 'apostatic selection'. Evidence exists to indicate search image formation by predators and apostatic selection operating on wild prey populations, though not to demonstrate search image use directly resulting in apostatic selection. The present study attempted to address this deficiency, using British Lepidoptera active in winter as a model system. It has been proposed that the typically polymorphic wing colouration of these species represents an anti-search image adaptation against birds. To test (a) for search image-driven apostatic selection, dimorphic populations of artificial moth-like models were established in woodland at varying relative morph frequencies and exposed to predation by natural populations of birds. In addition, to test (b) whether abundance and degree of polymorphism are correlated across British winter-active moths, as predicted where search image use drives apostatic selection, a series of phylogenetic comparative analyses were conducted. There was a positive relationship between artificial morph frequency and probability of predation, consistent with birds utilizing search images and exerting apostatic selection. Abundance and degree of polymorphism were found to be positively correlated across British Lepidoptera active in winter, though not across all taxonomic groups analysed. This evidence is consistent with polymorphism in this group having evolved in response to search image-driven apostatic selection and supports the viability of this mechanism as a means by which phenotypic and genetic variation may be maintained in natural populations. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Artificial Intelligence Software Engineering (AISE) model

NASA Technical Reports Server (NTRS)

Kiss, Peter A.

1990-01-01

The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

Organically Grown Architectures: Creating Decentralized, Autonomous Systems by Embryomorphic Engineering

NASA Astrophysics Data System (ADS)

Doursat, René

Exploding growth growth in computational systems forces us to gradually replace rigid design and control with decentralization and autonomy. Information technologies will progress, instead, by"meta-designing" mechanisms of system self-assembly, self-regulation and evolution. Nature offers a great variety of efficient complex systems, in which numerous small elements form large-scale, adaptive patterns. The new engineering challenge is to recreate this self-organization and let it freely generate innovative designs under guidance. This article presents an original model of artificial system growth inspired by embryogenesis. A virtual organism is a lattice of cells that proliferate, migrate and self-pattern into differentiated domains. Each cell's fate is controlled by an internal gene regulatory network network. Embryomorphic engineering emphasizes hyperdistributed architectures, and their development as a prerequisite of evolutionary design.

Evaluating neural networks and artificial intelligence systems

NASA Astrophysics Data System (ADS)

Alberts, David S.

1994-02-01

Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

Effects of topology on network evolution

NASA Astrophysics Data System (ADS)

Oikonomou, Panos; Cluzel, Philippe

2006-08-01

The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage. A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks. Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems. Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection. We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Evolutionary perspectives on clonal reproduction in vertebrate animals

PubMed Central

Avise, John C.

2015-01-01

A synopsis is provided of different expressions of whole-animal vertebrate clonality (asexual organismal-level reproduction), both in the laboratory and in nature. For vertebrate taxa, such clonal phenomena include the following: human-mediated cloning via artificial nuclear transfer; intergenerational clonality in nature via parthenogenesis and gynogenesis; intergenerational hemiclonality via hybridogenesis and kleptogenesis; intragenerational clonality via polyembryony; and what in effect qualifies as clonal replication via self-fertilization and intense inbreeding by simultaneous hermaphrodites. Each of these clonal or quasi-clonal mechanisms is described, and its evolutionary genetic ramifications are addressed. By affording an atypical vantage on standard vertebrate reproduction, clonality offers fresh perspectives on the evolutionary and ecological significance of recombination-derived genetic variety. PMID:26195735

Artificial Blood Substitutes: First Steps on the Long Route to Clinical Utility

PubMed Central

Moradi, Samira; Jahanian-Najafabadi, Ali; Roudkenar, Mehryar Habibi

2016-01-01

The 21st century is challenging for human beings. Increased population growth, population aging, generation of new infectious agents, and natural disasters are some threatening factors for the current state of blood transfusion. However, it seems that science and technology not only could overcome these challenges but also would turn many human dreams to reality in this regard. Scientists believe that one of the future evolutionary innovations could be artificial blood substitutes that might pave the way to a new era in transfusion medicine. In this review, recent status and progresses in artificial blood substitutes, focusing on red blood cells substitutes, are summarized. In addition, steps taken toward the development of artificial blood technology and some of their promises and hurdles will be highlighted. However, it must be noted that artificial blood is still at the preliminary stages of development, and to fulfill this dream, ie, to routinely transfuse artificial blood into human vessels, we still have to strengthen our knowledge and be patient. PMID:27812292

The Genome of the Sea Urchin Strongylocentrotus purpuratus

PubMed Central

2011-01-01

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus, a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes. PMID:17095691

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks.

PubMed

Feng, Lei; Zhu, Susu; Lin, Fucheng; Su, Zhenzhu; Yuan, Kangpei; Zhao, Yiying; He, Yong; Zhang, Chu

2018-06-15

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

NASA Astrophysics Data System (ADS)

Niu, Chaojun; Han, Xiang'e.

2015-10-01

Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

Artificial gravity in space and in medical research

NASA Technical Reports Server (NTRS)

Cardus, D.

1994-01-01

The history of manned space flight has repeatedly documented the fact that prolonged sojourn in space causes physiological deconditioning. Physiological deterioration has raised a legitimate concern about man's ability to adequately perform in the course of long missions and even the possibility of leading to circumstances threatening survival. One of the possible countermeasures of physiological deconditioning, theoretically more complete than others presently used since it affects all bodily systems, is artificial gravity. Space stations and spacecrafts can be equipped with artificial gravity, but is artificial gravity necessary? The term "necessary" must be qualified because a meaningful answer to the question depends entirely on further defining the purpose of space travel. If man intends to stay only temporarily in space, then he must keep himself in good physical condition so as to be able to return to earth or to land on any other planetary surface without undue exposure to major physiological problems resulting from transition through variable gravitational fields. Such a situation makes artificial gravity highly desirable, although perhaps not absolutely necessary in the case of relative short exposure to microgravity, but certainly necessary in interplanetary flight and planetary landings. If the intent is to remain indefinitely in space, to colonize space, then artificial gravity may not be necessary, but in this case the consequences of long term effects of adaptation to weightlessness will have to be weighed against the biological evolutionary outcomes that are to be expected. At the moment, plans for establishing permanent colonies in space seem still remote. More likely, the initial phase of exploration of the uncharted solar system will take place through successive, scope limited, research ventures ending with return to earth. This will require man to be ready to operate in gravitational fields of variable intensity. Equipping spacecrafts or space stations with some means of artificial gravity in this initial phase is, therefore, necessary without question. In a strict sense artificial gravity is conceived as a means of replacing natural gravity in space by the centripetal acceleration generated by some sort of rotating device. Rotating devices create an inertial force which has effects on bodies similar to those caused by terrestrial gravity, but artificial gravity by a rotation device is not the same as terrestrial gravity, as we shall see. Present research in artificial gravity for space exploration is projected in two main directions: artificial gravity for whole space stations and artificial gravity produced by short arm centrifuges designed for human use in space.

Artificial gravity in space and in medical research.

PubMed

Cardús, D

1994-05-01

The history of manned space flight has repeatedly documented the fact that prolonged sojourn in space causes physiological deconditioning. Physiological deterioration has raised a legitimate concern about man's ability to adequately perform in the course of long missions and even the possibility of leading to circumstances threatening survival. One of the possible countermeasures of physiological deconditioning, theoretically more complete than others presently used since it affects all bodily systems, is artificial gravity. Space stations and spacecrafts can be equipped with artificial gravity, but is artificial gravity necessary? The term "necessary" must be qualified because a meaningful answer to the question depends entirely on further defining the purpose of space travel. If man intends to stay only temporarily in space, then he must keep himself in good physical condition so as to be able to return to earth or to land on any other planetary surface without undue exposure to major physiological problems resulting from transition through variable gravitational fields. Such a situation makes artificial gravity highly desirable, although perhaps not absolutely necessary in the case of relative short exposure to microgravity, but certainly necessary in interplanetary flight and planetary landings. If the intent is to remain indefinitely in space, to colonize space, then artificial gravity may not be necessary, but in this case the consequences of long term effects of adaptation to weightlessness will have to be weighed against the biological evolutionary outcomes that are to be expected. At the moment, plans for establishing permanent colonies in space seem still remote. More likely, the initial phase of exploration of the uncharted solar system will take place through successive, scope limited, research ventures ending with return to earth. This will require man to be ready to operate in gravitational fields of variable intensity. Equipping spacecrafts or space stations with some means of artificial gravity in this initial phase is, therefore, necessary without question. In a strict sense artificial gravity is conceived as a means of replacing natural gravity in space by the centripetal acceleration generated by some sort of rotating device. Rotating devices create an inertial force which has effects on bodies similar to those caused by terrestrial gravity, but artificial gravity by a rotation device is not the same as terrestrial gravity, as we shall see. Present research in artificial gravity for space exploration is projected in two main directions: artificial gravity for whole space stations and artificial gravity produced by short arm centrifuges designed for human use in space.

Intelligent reservoir operation system based on evolving artificial neural networks

NASA Astrophysics Data System (ADS)

Chaves, Paulo; Chang, Fi-John

2008-06-01

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

The application of artificial intelligence in the optimal design of mechanical systems

NASA Astrophysics Data System (ADS)

Poteralski, A.; Szczepanik, M.

2016-11-01

The paper is devoted to new computational techniques in mechanical optimization where one tries to study, model, analyze and optimize very complex phenomena, for which more precise scientific tools of the past were incapable of giving low cost and complete solution. Soft computing methods differ from conventional (hard) computing in that, unlike hard computing, they are tolerant of imprecision, uncertainty, partial truth and approximation. The paper deals with an application of the bio-inspired methods, like the evolutionary algorithms (EA), the artificial immune systems (AIS) and the particle swarm optimizers (PSO) to optimization problems. Structures considered in this work are analyzed by the finite element method (FEM), the boundary element method (BEM) and by the method of fundamental solutions (MFS). The bio-inspired methods are applied to optimize shape, topology and material properties of 2D, 3D and coupled 2D/3D structures, to optimize the termomechanical structures, to optimize parameters of composites structures modeled by the FEM, to optimize the elastic vibrating systems to identify the material constants for piezoelectric materials modeled by the BEM and to identify parameters in acoustics problem modeled by the MFS.

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

PubMed

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

Niche construction, sources of selection and trait coevolution.

PubMed

Laland, Kevin; Odling-Smee, John; Endler, John

2017-10-06

Organisms modify and choose components of their local environments. This 'niche construction' can alter ecological processes, modify natural selection and contribute to inheritance through ecological legacies. Here, we propose that niche construction initiates and modifies the selection directly affecting the constructor, and on other species, in an orderly, directed and sustained manner. By dependably generating specific environmental states, niche construction co-directs adaptive evolution by imposing a consistent statistical bias on selection. We illustrate how niche construction can generate this evolutionary bias by comparing it with artificial selection. We suggest that it occupies the middle ground between artificial and natural selection. We show how the perspective leads to testable predictions related to: (i) reduced variance in measures of responses to natural selection in the wild; (ii) multiple trait coevolution, including the evolution of sequences of traits and patterns of parallel evolution; and (iii) a positive association between niche construction and biodiversity. More generally, we submit that evolutionary biology would benefit from greater attention to the diverse properties of all sources of selection.

Neural network explanation using inversion.

PubMed

Saad, Emad W; Wunsch, Donald C

2007-01-01

An important drawback of many artificial neural networks (ANN) is their lack of explanation capability [Andrews, R., Diederich, J., & Tickle, A. B. (1996). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389]. This paper starts with a survey of algorithms which attempt to explain the ANN output. We then present HYPINV, a new explanation algorithm which relies on network inversion; i.e. calculating the ANN input which produces a desired output. HYPINV is a pedagogical algorithm, that extracts rules, in the form of hyperplanes. It is able to generate rules with arbitrarily desired fidelity, maintaining a fidelity-complexity tradeoff. To our knowledge, HYPINV is the only pedagogical rule extraction method, which extracts hyperplane rules from continuous or binary attribute neural networks. Different network inversion techniques, involving gradient descent as well as an evolutionary algorithm, are presented. An information theoretic treatment of rule extraction is presented. HYPINV is applied to example synthetic problems, to a real aerospace problem, and compared with similar algorithms using benchmark problems.

Evolutionary robotics simulations help explain why reciprocity is rare in nature

PubMed Central

André, Jean-Baptiste; Nolfi, Stefano

2016-01-01

The relative rarity of reciprocity in nature, contrary to theoretical predictions that it should be widespread, is currently one of the major puzzles in social evolution theory. Here we use evolutionary robotics to solve this puzzle. We show that models based on game theory are misleading because they neglect the mechanics of behavior. In a series of experiments with simulated robots controlled by artificial neural networks, we find that reciprocity does not evolve, and show that this results from a general constraint that likely also prevents it from evolving in the wild. Reciprocity can evolve if it requires very few mutations, as is usually assumed in evolutionary game theoretic models, but not if, more realistically, it requires the accumulation of many adaptive mutations. PMID:27616139

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 cortical bone fracture resistance curves by fusing artificial neural networks and linear regression.

PubMed

Vukicevic, Arso M; Jovicic, Gordana R; Jovicic, Milos N; Milicevic, Vladimir L; Filipovic, Nenad D

2018-02-01

Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R 2 = 0.82 vs. R 2 = 0.76) and Toughness threshold with ageing (R 2 = 0.73 vs. R 2 = 0.66).

EdiPy: a resource to simulate the evolution of plant mitochondrial genes under the RNA editing.

PubMed

Picardi, Ernesto; Quagliariello, Carla

2006-02-01

EdiPy is an online resource appropriately designed to simulate the evolution of plant mitochondrial genes in a biologically realistic fashion. EdiPy takes into account the presence of sites subjected to RNA editing and provides multiple artificial alignments corresponding to both genomic and cDNA sequences. Each artificial data set can successively be submitted to main and widespread evolutionary and phylogenetic software packages such as PAUP, Phyml, PAML and Phylip. As an online bioinformatic resource, EdiPy is available at the following web page: http://biologia.unical.it/py_script/index.html.

Hierarchical coordinate systems for understanding complexity and its evolution, with applications to genetic regulatory networks.

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

Automating software design system DESTA

NASA Technical Reports Server (NTRS)

Lovitsky, Vladimir A.; Pearce, Patricia D.

1992-01-01

'DESTA' is the acronym for the Dialogue Evolutionary Synthesizer of Turnkey Algorithms by means of a natural language (Russian or English) functional specification of algorithms or software being developed. DESTA represents the computer-aided and/or automatic artificial intelligence 'forgiving' system which provides users with software tools support for algorithm and/or structured program development. The DESTA system is intended to provide support for the higher levels and earlier stages of engineering design of software in contrast to conventional Computer Aided Design (CAD) systems which provide low level tools for use at a stage when the major planning and structuring decisions have already been taken. DESTA is a knowledge-intensive system. The main features of the knowledge are procedures, functions, modules, operating system commands, batch files, their natural language specifications, and their interlinks. The specific domain for the DESTA system is a high level programming language like Turbo Pascal 6.0. The DESTA system is operational and runs on an IBM PC computer.

An evolutionary morphological approach for software development cost estimation.

PubMed

Araújo, Ricardo de A; Oliveira, Adriano L I; Soares, Sergio; Meira, Silvio

2012-08-01

In this work we present an evolutionary morphological approach to solve the software development cost estimation (SDCE) problem. The proposed approach consists of a hybrid artificial neuron based on framework of mathematical morphology (MM) with algebraic foundations in the complete lattice theory (CLT), referred to as dilation-erosion perceptron (DEP). Also, we present an evolutionary learning process, called DEP(MGA), using a modified genetic algorithm (MGA) to design the DEP model, because a drawback arises from the gradient estimation of morphological operators in the classical learning process of the DEP, since they are not differentiable in the usual way. Furthermore, an experimental analysis is conducted with the proposed model using five complex SDCE problems and three well-known performance metrics, demonstrating good performance of the DEP model to solve SDCE problems. Copyright © 2012 Elsevier Ltd. All rights reserved.

The asthma epidemic and our artificial habitats

PubMed Central

Maziak, Wasim

2005-01-01

Background The recent increase in childhood asthma has been a puzzling one. Recent views focus on the role of infection in the education of the immune system of young children. However, this so called hygiene hypothesis fails to answer some important questions about the current trends in asthma or to account for environmental influences that bear little relation to infection. Discussion The multi-factorial nature of asthma, reflecting the different ways we tend to interact with our environment, mandates that we look at the asthma epidemic from a broader perspective. Seemingly modern affluent lifestyles are placing us increasingly in static, artificial, microenvironments very different from the conditions prevailed for most part of our evolution and shaped our organisms. Changes that occurred during the second half of the 20th century in industrialized nations with the spread of central heating/conditioning, building insulation, hygiene, TV/PC/games, manufactured food, indoor entertainment, cars, medical care, and sedentary lifestyles all seem to be depriving our children from the essential inputs needed to develop normal airway function (resistance). Asthma according to this view is a manifestation of our respiratory maladaptation to modern lifestyles, or in other words to our increasingly artificial habitats. The basis of the artificial habitat notion may lie in reduced exposure of innate immunity to a variety of environmental stimuli, infectious and non-infectious, leading to reduced formulation of regulatory cells/cytokines as well as inscribed regulatory pathways. This could contribute to a faulty checking mechanism of non-functional Th2 (and likely Th1) responses, resulting in asthma and other immuno-dysregulation disorders. Summary In this piece I discuss the artificial habitat concept, its correspondence with epidemiological data of asthma and allergy, and provide possible immunological underpinning for it from an evolutionary perspective of health and disease. PMID:15799786

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms.

PubMed

Chan, Wai Sum; Recknagel, Friedrich; Cao, Hongqing; Park, Ho-Dong

2007-05-01

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.

Modeling an aquatic ecosystem: application of an evolutionary algorithm with genetic doping to reduce prediction uncertainty

NASA Astrophysics Data System (ADS)

Friedel, Michael; Buscema, Massimo

2016-04-01

Aquatic ecosystem models can potentially be used to understand the influence of stresses on catchment resource quality. Given that catchment responses are functions of natural and anthropogenic stresses reflected in sparse and spatiotemporal biological, physical, and chemical measurements, an ecosystem is difficult to model using statistical or numerical methods. We propose an artificial adaptive systems approach to model ecosystems. First, an unsupervised machine-learning (ML) network is trained using the set of available sparse and disparate data variables. Second, an evolutionary algorithm with genetic doping is applied to reduce the number of ecosystem variables to an optimal set. Third, the optimal set of ecosystem variables is used to retrain the ML network. Fourth, a stochastic cross-validation approach is applied to quantify and compare the nonlinear uncertainty in selected predictions of the original and reduced models. Results are presented for aquatic ecosystems (tens of thousands of square kilometers) undergoing landscape change in the USA: Upper Illinois River Basin and Central Colorado Assessment Project Area, and Southland region, NZ.

A Powerful Toolkit for Synthetic Biology: Over 3.8 Billion Years of Evolution

NASA Technical Reports Server (NTRS)

Rothschild, Lynn J.

2010-01-01

The combination of evolutionary with engineering principles will enhance synthetic biology. Conversely, synthetic biology has the potential to enrich evolutionary biology by explaining why some adaptive space is empty, on Earth or elsewhere. Synthetic biology, the design and construction of artificial biological systems, substitutes bio-engineering for evolution, which is seen as an obstacle. But because evolution has produced the complexity and diversity of life, it provides a proven toolkit of genetic materials and principles available to synthetic biology. Evolution operates on the population level, with the populations composed of unique individuals that are historical entities. The source of genetic novelty includes mutation, gene regulation, sex, symbiosis, and interspecies gene transfer. At a phenotypic level, variation derives from regulatory control, replication and diversification of components, compartmentalization, sexual selection and speciation, among others. Variation is limited by physical constraints such as diffusion, and chemical constraints such as reaction rates and membrane fluidity. While some of these tools of evolution are currently in use in synthetic biology, all ought to be examined for utility. A hybrid approach of synthetic biology coupled with fine-tuning through evolution is suggested

Neighbourhood reaction in the evolution of cooperation.

PubMed

Yang, Guoli; Zhang, Weiming; Xiu, Baoxin

2015-05-07

Combining evolutionary games with adaptive networks, an entangled model between strategy evolution and structure adaptation is researched in this paper. We consider a large population of cooperators C and defectors D placed in the networks, playing the repeated prisoner׳s dilemma (PD) games. Because of the conflicts between social welfare and personal rationality, both strategy and structure are allowed to change. In this paper, the dynamics of strategy originates form the partner imitation based on social learning and the dynamics of structure is driven by the active linking and neighbourhood reaction. Notably, the neighbourhood reaction is investigated considering the changes of interfaces between cooperators and defectors, where some neighbours may get away from the interface once the focal agent changes to different strategy. A rich landscape is demonstrated by changing various embedding parameters, which sheds light upon that reacting promptly to the shifted neighbour will promote the prevalence of cooperation. Our model encapsulates the dynamics of strategy, reaction and structure into the evolutionary games, which manifests some intriguing principles in the competition between two groups in natural populations, artificial systems and even human societies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Applying Evolutionary Thinking to the Study of Emotion

PubMed Central

Weisfeld, Glenn E.; Goetz, Stefan M. M.

2013-01-01

This paper argues for invoking evolutionary, functional thinking in analyzing emotions. It suggests that the fitness needs of normal individuals be kept in mind when trying to understand emotional behavior. This point of view is elaborated in sections addressing these topics: defining emotion; applying comparative analysis to the study of emotions; focusing on the elicitors and resulting motivated behaviors mediated by the various affects; recognizing that not all emotions have prominent, distinct facial expressions; acknowledging all of the basic emotions and not just some exemplars; crediting the more sensible Cannon-Bard theory over James-Lange; recognizing the more ancient, fundamental role of the limbic system in emotion compared with that of the neocortex; and analyzing socio-emotional interactions as they occur naturally, not just individual emotional behavior studied under artificial conditions. Describing the various facets and neuroendocrine mechanisms of each basic emotion can provide a framework for understanding the normal and pathological development of each emotion. Such an inventory, or ethogram, would provide a comprehensive list of all of the observable behavioral tendencies of our species. PMID:25379244

A powerful toolkit for synthetic biology: Over 3.8 billion years of evolution.

PubMed

Rothschild, Lynn J

2010-04-01

The combination of evolutionary with engineering principles will enhance synthetic biology. Conversely, synthetic biology has the potential to enrich evolutionary biology by explaining why some adaptive space is empty, on Earth or elsewhere. Synthetic biology, the design and construction of artificial biological systems, substitutes bio-engineering for evolution, which is seen as an obstacle. But because evolution has produced the complexity and diversity of life, it provides a proven toolkit of genetic materials and principles available to synthetic biology. Evolution operates on the population level, with the populations composed of unique individuals that are historical entities. The source of genetic novelty includes mutation, gene regulation, sex, symbiosis, and interspecies gene transfer. At a phenotypic level, variation derives from regulatory control, replication and diversification of components, compartmentalization, sexual selection and speciation, among others. Variation is limited by physical constraints such as diffusion, and chemical constraints such as reaction rates and membrane fluidity. While some of these tools of evolution are currently in use in synthetic biology, all ought to be examined for utility. A hybrid approach of synthetic biology coupled with fine-tuning through evolution is suggested.

Mesoscopic structure conditions the emergence of cooperation on social networks

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

Lozano, S.; Arenas, A.; Sanchez, A.

We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement withmore » the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.« less

Basic emotions and adaptation. A computational and evolutionary model.

PubMed

Pacella, Daniela; Ponticorvo, Michela; Gigliotta, Onofrio; Miglino, Orazio

2017-01-01

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual "sensations" based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual's life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.

A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences.

PubMed

Karaboga, D; Aslan, S

2016-04-27

The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

Optimal integrated management of groundwater resources and irrigated agriculture in arid coastal regions

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Heck, V.

2014-09-01

Groundwater systems in arid coastal regions are particularly at risk due to limited potential for groundwater replenishment and increasing water demand, caused by a continuously growing population. For ensuring a sustainable management of those regions, we developed a new simulation-based integrated water management system. The management system unites process modelling with artificial intelligence tools and evolutionary optimisation techniques for managing both water quality and water quantity of a strongly coupled groundwater-agriculture system. Due to the large number of decision variables, a decomposition approach is applied to separate the original large optimisation problem into smaller, independent optimisation problems which finally allow for faster and more reliable solutions. It consists of an analytical inner optimisation loop to achieve a most profitable agricultural production for a given amount of water and an outer simulation-based optimisation loop to find the optimal groundwater abstraction pattern. Thereby, the behaviour of farms is described by crop-water-production functions and the aquifer response, including the seawater interface, is simulated by an artificial neural network. The methodology is applied exemplarily for the south Batinah re-gion/Oman, which is affected by saltwater intrusion into a coastal aquifer system due to excessive groundwater withdrawal for irrigated agriculture. Due to contradicting objectives like profit-oriented agriculture vs aquifer sustainability, a multi-objective optimisation is performed which can provide sustainable solutions for water and agricultural management over long-term periods at farm and regional scales in respect of water resources, environment, and socio-economic development.

How to Handle Speciose Clades? Mass Taxon-Sampling as a Strategy towards Illuminating the Natural History of Campanula (Campanuloideae)

PubMed Central

Mansion, Guilhem; Parolly, Gerald; Crowl, Andrew A.; Mavrodiev, Evgeny; Cellinese, Nico; Oganesian, Marine; Fraunhofer, Katharina; Kamari, Georgia; Phitos, Dimitrios; Haberle, Rosemarie; Akaydin, Galip; Ikinci, Nursel; Raus, Thomas; Borsch, Thomas

2012-01-01

Background Speciose clades usually harbor species with a broad spectrum of adaptive strategies and complex distribution patterns, and thus constitute ideal systems to disentangle biotic and abiotic causes underlying species diversification. The delimitation of such study systems to test evolutionary hypotheses is difficult because they often rely on artificial genus concepts as starting points. One of the most prominent examples is the bellflower genus Campanula with some 420 species, but up to 600 species when including all lineages to which Campanula is paraphyletic. We generated a large alignment of petD group II intron sequences to include more than 70% of described species as a reference. By comparison with partial data sets we could then assess the impact of selective taxon sampling strategies on phylogenetic reconstruction and subsequent evolutionary conclusions. Methodology/Principal Findings Phylogenetic analyses based on maximum parsimony (PAUP, PRAP), Bayesian inference (MrBayes), and maximum likelihood (RAxML) were first carried out on the large reference data set (D680). Parameters including tree topology, branch support, and age estimates, were then compared to those obtained from smaller data sets resulting from “classification-guided” (D088) and “phylogeny-guided sampling” (D101). Analyses of D088 failed to fully recover the phylogenetic diversity in Campanula, whereas D101 inferred significantly different branch support and age estimates. Conclusions/Significance A short genomic region with high phylogenetic utility allowed us to easily generate a comprehensive phylogenetic framework for the speciose Campanula clade. Our approach recovered 17 well-supported and circumscribed sub-lineages. Knowing these will be instrumental for developing more specific evolutionary hypotheses and guide future research, we highlight the predictive value of a mass taxon-sampling strategy as a first essential step towards illuminating the detailed evolutionary history of diverse clades. PMID:23209646

Which evolutionary status does the Blue Large-Amplitude Pulsators stay at?

NASA Astrophysics Data System (ADS)

Wu, Tao; Li, Yan

2018-05-01

Asteroseismology is a very useful tool for exploring the stellar interiors and evolutionary status and for determining stellar fundamental parameters, such as stellar mass, radius, surface gravity, and the stellar mean density. In the present work, we use it to preliminarily analyze the 14 new-type pulsating stars: Blue Large-Amplitude Pulsators (BLAPs) which is observed by OGLE project, to roughly analyze their evolutionary status. We adopt the theory of single star evolution and artificially set the mass loss rate of \\dot{M}=-2× 10^{-4} M_{⊙}/year and mass loss beginning at the radius of R = 40 R_{⊙} on red giant branch to generate a series of theoretical models. Based on these theoretical models and the corresponding observations, we find that those BLAP stars are more likely to be the core helium burning stars. Most of them are in the middle and late phase of the helium burning.

Space exploration and colonization - Towards a space faring society

NASA Technical Reports Server (NTRS)

Hammond, Walter E.

1990-01-01

Development trends of space exploration and colonization since 1957 are reviewed, and a five-phase evolutionary program planned for the long-term future is described. The International Geosphere-Biosphere program which is intended to provide the database on enviromental changes of the earth as a global system is considered. Evolution encompasses the anticipated advantages of such NASA observation projects as the Hubble Space Telescope, the Gamma Ray Observatory, the Advanced X-Ray Astrophysics Facility, and the Cosmic Background Explorer. Attention is given to requirements for space colonization, including development of artificial gravity and countermeasures to mitigate zero gravity problems; robotics and systems aimed to minimize human exposure to the space environment; the use of nuclear propulsion; and international collaboration on lunar-Mars projects. It is recommended that nuclear energy sources be developed for both propulsion and as extraterrestrial power plants.

A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks

NASA Astrophysics Data System (ADS)

Chang, Chen-Sung

This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.

The emergence of mind and brain: an evolutionary, computational, and philosophical approach.

PubMed

Mainzer, Klaus

2008-01-01

Modern philosophy of mind cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classical philosophy of formal languages as well as symbolic AI assume that all kinds of knowledge must explicitly be represented by formal or programming languages. This assumption is limited by recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge, which is learnt by doing and understood by bodily interacting with changing environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. The embodied mind is considered an emergent capacity of the brain as a self-organizing complex system. Actually, self-organization has been a successful strategy of evolution to handle the increasing complexity of the world. Genetic programs are not sufficient and cannot prepare the organism for all kinds of complex situations in the future. Self-organization and emergence are fundamental concepts in the theory of complex dynamical systems. They are also applied in organic computing as a recent research field of computer science. Therefore, cognitive science, AI, and robotics try to model the embodied mind in an artificial evolution. The paper analyzes these approaches in the interdisciplinary framework of complex dynamical systems and discusses their philosophical impact.

Growing Up of Autonomous Agents: an Emergent Phenomenon

NASA Astrophysics Data System (ADS)

Morgavi, Giovanna; Marconi, Lucia

2008-10-01

A fundamental research challenge is the design of robust artifacts that are capable of operating under changing environments and noisy input, and yet exhibit the desired behavior and response time. These systems should be able to adapt and learn how to react to unforeseen scenarios as well as to display properties comparable to biological entities. The turn to nature has brought us many unforeseen great concepts. Biological systems are able to handle many of these challenges with an elegance and efficiency still far beyond current human artifacts. A living artifact grows up when its capabilities, abilities/knowledge, shift to a further level of complexity, i.e. the complexity rank of its internal capabilities performs a step forward. In the attempt to define an architecture for autonomous growing up agents [1]. We conducted an experiment on the abstraction process in children as natural parts of a cognitive system. We found that linguistic growing up involve a number of different trial processes. We identified a fixed number of distinct paths that were crossed by children. Once a given interpretation paths was discovered useless, they tried to follow another path, until the new meaning was emerging. This study generates suggestion about the evolutionary conditions conducive to the emergence of growing up in robots and provides guidelines for designing artificial evolutionary systems displaying spontaneous adaptation abilities. The importance of multi-sensor perception, motivation and emotional drives are underlined and, above all, the growing up insights shows similarities to emergent self-organized behaviors.

Multirobot Lunar Excavation and ISRU Using Artificial-Neural-Tissue Controllers

NASA Astrophysics Data System (ADS)

Thangavelautham, Jekanthan; Smith, Alexander; Abu El Samid, Nader; Ho, Alexander; Boucher, Dale; Richard, Jim; D'Eleuterio, Gabriele M. T.

2008-01-01

Automation of site preparation and resource utilization on the Moon with teams of autonomous robots holds considerable promise for establishing a lunar base. Such multirobot autonomous systems would require limited human support infrastructure, complement necessary manned operations and reduce overall mission risk. We present an Artificial Neural Tissue (ANT) architecture as a control system for autonomous multirobot excavation tasks. An ANT approach requires much less human supervision and pre-programmed human expertise than previous techniques. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to `breed' controllers for the task at hand in simulation and the fittest controllers are transferred onto hardware for further validation and testing. ANT facilitates `machine creativity', with the emergence of novel functionality through a process of self-organized task decomposition of mission goals. ANT based controllers are shown to exhibit self-organization, employ stigmergy (communication mediated through the environment) and make use of templates (unlabeled environmental cues). With lunar in-situ resource utilization (ISRU) efforts in mind, ANT controllers have been tested on a multirobot excavation task in which teams of robots with no explicit supervision can successfully avoid obstacles, interpret excavation blueprints, perform layered digging, avoid burying or trapping other robots and clear/maintain digging routes.

Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention.

PubMed

Johnston, Iain G; Williams, Ben P

2016-02-24

Since their endosymbiotic origin, mitochondria have lost most of their genes. Although many selective mechanisms underlying the evolution of mitochondrial genomes have been proposed, a data-driven exploration of these hypotheses is lacking, and a quantitatively supported consensus remains absent. We developed HyperTraPS, a methodology coupling stochastic modeling with Bayesian inference, to identify the ordering of evolutionary events and suggest their causes. Using 2015 complete mitochondrial genomes, we inferred evolutionary trajectories of mtDNA gene loss across the eukaryotic tree of life. We find that proteins comprising the structural cores of the electron transport chain are preferentially encoded within mitochondrial genomes across eukaryotes. A combination of high GC content and high protein hydrophobicity is required to explain patterns of mtDNA gene retention; a model that accounts for these selective pressures can also predict the success of artificial gene transfer experiments in vivo. This work provides a general method for data-driven inference of the ordering of evolutionary and progressive events, here identifying the distinct features shaping mitochondrial genomes of present-day species. Copyright © 2016 Elsevier Inc. All rights reserved.

General visual robot controller networks via artificial evolution

NASA Astrophysics Data System (ADS)

Cliff, David; Harvey, Inman; Husbands, Philip

1993-08-01

We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.

Going mobile: non-cell-autonomous small RNAs shape the genetic landscape of plants.

PubMed

Pyott, Douglas E; Molnar, Attila

2015-04-01

RNA silencing is a form of genetic regulation, which is conserved across eukaryotes and has wide ranging biological functions. Recently, there has been a growing appreciation for the importance of mobility in RNA silencing pathways, particularly in plants. Moreover, in addition to the importance for mobile RNA silencing in an evolutionary context, the potential for utilizing mobile short silencing RNAs in biotechnological applications is becoming apparent. This review aims to set current knowledge of this topic in a historical context and provides examples to illustrate the importance of mobile RNA silencing in both natural and artificially engineered systems in plants. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Does the queen win it all? Queen-worker conflict over male production in the bumblebee, Bombus terrestris

NASA Astrophysics Data System (ADS)

Alaux, Cédric; Savarit, Fabrice; Jaisson, Pierre; Hefetz, Abraham

Social insects provide a useful model for studying the evolutionary balance between cooperation and conflict linked to genetic structure. We investigated the outcome of this conflict in the bumblebee, Bombus terrestris, whose annual colony life cycle is characterized by overt competition over male production. We established artificial colonies composed of a queen and unrelated workers by daily exchange of callow workers between colony pairs of distinct genetic make-up. Using microsatellite analysis, this procedure allowed an exact calculation of the proportion of worker-derived males. The development and social behavior of these artificial colonies were similar to those of normal colonies. Despite a high worker reproduction attempt (63.8% of workers had developed ovaries and 38.4% were egg-layers), we found that on average 95% of the males produced during the competition phase (CPh) were queen-derived. However, in four colonies, queen death resulted in a considerable amount of worker-derived male production. The different putative ultimate causes of this efficient control by the queen are discussed, and we suggest a possible scenario of an evolutionary arms race that may occur between these two female castes.

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Tetrapods on the EDGE: Overcoming data limitations to identify phylogenetic conservation priorities

PubMed Central

Gray, Claudia L.; Wearn, Oliver R.; Owen, Nisha R.

2018-01-01

The scale of the ongoing biodiversity crisis requires both effective conservation prioritisation and urgent action. As extinction is non-random across the tree of life, it is important to prioritise threatened species which represent large amounts of evolutionary history. The EDGE metric prioritises species based on their Evolutionary Distinctiveness (ED), which measures the relative contribution of a species to the total evolutionary history of their taxonomic group, and Global Endangerment (GE), or extinction risk. EDGE prioritisations rely on adequate phylogenetic and extinction risk data to generate meaningful priorities for conservation. However, comprehensive phylogenetic trees of large taxonomic groups are extremely rare and, even when available, become quickly out-of-date due to the rapid rate of species descriptions and taxonomic revisions. Thus, it is important that conservationists can use the available data to incorporate evolutionary history into conservation prioritisation. We compared published and new methods to estimate missing ED scores for species absent from a phylogenetic tree whilst simultaneously correcting the ED scores of their close taxonomic relatives. We found that following artificial removal of species from a phylogenetic tree, the new method provided the closest estimates of their “true” ED score, differing from the true ED score by an average of less than 1%, compared to the 31% and 38% difference of the previous methods. The previous methods also substantially under- and over-estimated scores as more species were artificially removed from a phylogenetic tree. We therefore used the new method to estimate ED scores for all tetrapods. From these scores we updated EDGE prioritisation rankings for all tetrapod species with IUCN Red List assessments, including the first EDGE prioritisation for reptiles. Further, we identified criteria to identify robust priority species in an effort to further inform conservation action whilst limiting uncertainty and anticipating future phylogenetic advances. PMID:29641585

Duplicated Female Receptacle Organs for Traumatic Insemination in the Tropical Bed Bug Cimex hemipterus: Adaptive Variation or Malformation?

PubMed Central

Kamimura, Yoshitaka; Mitsumoto, Hiroyuki; Lee, Chow-Yang

2014-01-01

During mating, male bed bugs (Cimicidae) pierce the female abdomen to inject sperm using their needle-like genitalia. Females evolved specialized paragenital organs (the spermalege and associated structures) to receive traumatically injected ejaculates. In Leptocimex duplicatus, the spermalege is duplicated, but the evolutionary significance of this is unclear. In Cimex hemipterus and C. lectularius, in which females normally develop a single spermalege on the right side of the abdomen, similar duplication sometimes occurs. Using these aberrant morphs (D-females) of C. hemipterus, we tested the hypothesis that both of the duplicated spermaleges are functionally competent. Scars on female abdominal exoskeletons indicated frequent misdirected piercing by male genitalia. However, the piercing sites showed a highly biased distribution towards the right side of the female body. A mating experiment showed that when the normal insemination site (the right-side spermalege) was artificially covered, females remained unfertilized. This was true even when females also had a spermalege on the left side (D-females). This result was attributed to handedness in male mating behavior. Irrespective of the observed disuse of the left-side spermalege by males for insemination, histological examination failed to detect any differences between the right-side and left-side spermaleges. Moreover, an artificial insemination experiment confirmed that spermatozoa injected into the left-side spermalege show apparently normal migration behavior to the female reproductive organs, indicating an evolutionary potential for functionally-competent duplicated spermaleges. We discuss possible mechanisms for the evolutionary maintenance of D-females and propose a plausible route to the functionally-competent duplicated spermaleges observed in L. duplicatus. PMID:24586643

A model for a knowledge-based system's life cycle

NASA Technical Reports Server (NTRS)

Kiss, Peter A.

1990-01-01

The American Institute of Aeronautics and Astronautics has initiated a Committee on Standards for Artificial Intelligence. Presented here are the initial efforts of one of the working groups of that committee. The purpose here is to present a candidate model for the development life cycle of Knowledge Based Systems (KBS). The intent is for the model to be used by the Aerospace Community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are detailed as are and the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

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.

Genome Diversity and Evolution in the Budding Yeasts (Saccharomycotina)

PubMed Central

Dujon, Bernard A.; Louis, Edward J.

2017-01-01

Considerable progress in our understanding of yeast genomes and their evolution has been made over the last decade with the sequencing, analysis, and comparisons of numerous species, strains, or isolates of diverse origins. The role played by yeasts in natural environments as well as in artificial manufactures, combined with the importance of some species as model experimental systems sustained this effort. At the same time, their enormous evolutionary diversity (there are yeast species in every subphylum of Dikarya) sparked curiosity but necessitated further efforts to obtain appropriate reference genomes. Today, yeast genomes have been very informative about basic mechanisms of evolution, speciation, hybridization, domestication, as well as about the molecular machineries underlying them. They are also irreplaceable to investigate in detail the complex relationship between genotypes and phenotypes with both theoretical and practical implications. This review examines these questions at two distinct levels offered by the broad evolutionary range of yeasts: inside the best-studied Saccharomyces species complex, and across the entire and diversified subphylum of Saccharomycotina. While obviously revealing evolutionary histories at different scales, data converge to a remarkably coherent picture in which one can estimate the relative importance of intrinsic genome dynamics, including gene birth and loss, vs. horizontal genetic accidents in the making of populations. The facility with which novel yeast genomes can now be studied, combined with the already numerous available reference genomes, offer privileged perspectives to further examine these fundamental biological questions using yeasts both as eukaryotic models and as fungi of practical importance. PMID:28592505

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.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

Biological Effects Of Artificial Illumination

NASA Astrophysics Data System (ADS)

Corth, Richard

1980-10-01

We are increasingly being warned of the possible effects of so called "polluted" light, that is light that differs in spectral content from that of sunlight. We should be concerned, we are told, because all animals and plants have evolved under this natural daylight and therefore any difference between that illuminant and the artificial illuminants that are on the market today, is suspect. The usual presentation of the differences between the sunlight and the artificial illuminants are as shown in Figure 1. Here we are shown the spectral power distribution of sunlight and Cool White fluorescent light. The spectral power distributions of each have been normalized to some convenient wavelength so that each can be seen and easily compared on the same figure. But this presentation is misleading for one does not experience artificial illuminants at the same intensity as one experiences sunlight. Sunlight intensities are ordinarily found to be in the 8000 to 10,000 footcandle range whereas artificial illuminants are rarely experienced at intensity levels greater than 100 footcandles. Therefore a representative difference between the two types of illumination conditions is more accurately represented as in Figure 2. Thus if evolutionary adaptations require that humans and other animals be exposed to sunlight to ensure wellbeing, it is clear that one must be exposed to sunlight intensities. It is not feasible to expect that artificially illuminated environments will be lit to the same intensity as sunlight

Basic emotions and adaptation. A computational and evolutionary model

PubMed Central

2017-01-01

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual “sensations” based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual’s life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions. PMID:29107988

Submolecular Gates Self-Assemble for Hot-Electron Transfer in Proteins.

PubMed

Filip-Granit, Neta; Goldberg, Eran; Samish, Ilan; Ashur, Idan; van der Boom, Milko E; Cohen, Hagai; Scherz, Avigdor

2017-07-27

Redox reactions play key roles in fundamental biological processes. The related spatial organization of donors and acceptors is assumed to undergo evolutionary optimization facilitating charge mobilization within the relevant biological context. Experimental information from submolecular functional sites is needed to understand the organization strategies and driving forces involved in the self-development of structure-function relationships. Here we exploit chemically resolved electrical measurements (CREM) to probe the atom-specific electrostatic potentials (ESPs) in artificial arrays of bacteriochlorophyll (BChl) derivatives that provide model systems for photoexcited (hot) electron donation and withdrawal. On the basis of computations we show that native BChl's in the photosynthetic reaction center (RC) self-assemble at their ground-state as aligned gates for functional charge transfer. The combined computational and experimental results further reveal how site-specific polarizability perpendicular to the molecular plane enhances the hot-electron transport. Maximal transport efficiency is predicted for a specific, ∼5 Å, distance above the center of the metalized BChl, which is in remarkably close agreement with the distance and mutual orientation of corresponding native cofactors. These findings provide new metrics and guidelines for analysis of biological redox centers and for designing charge mobilizing machines such as artificial photosynthesis.

Iteration expansion and regional evolution: phylogeography of Dendrobium officinale and four related taxa in southern China

PubMed Central

Hou, Beiwei; Luo, Jing; Zhang, Yusi; Niu, Zhitao; Xue, Qingyun; Ding, Xiaoyu

2017-01-01

The genus Dendrobium was used as a case study to elucidate the evolutionary history of Orchidaceae in the Sino-Japanese Floristic Region (SJFR) and Southeast Asia region. These evolutionary histories remain largely unknown, including the temporal and spatial distribution of the evolutionary events. The present study used nuclear and plastid DNA to determine the phylogeography of Dendrobium officinale and four closely related taxa. Plastid DNA haplotype and nuclear data were shown to be discordant, suggesting reticulate evolution drove the species’ diversification. Rapid radiation and genetic drift appeared to drive the evolution of D. tosaense and D. flexicaule, whereas introgression or hybridization might have been involved in the evolution of D. scoriarum and D. shixingense. The phylogeographical structure of D. officinale revealed that core natural distribution regions might have served as its glacial refuges. In recent years, human disturbances caused its artificial migration and population extinction. The five taxa may have originated from the Nanling Mountains and the Yungui Plateau and then migrated northward or eastward. After the initial iteration expansion, D. officinale populations appeared to experience the regional evolutionary patterns in different regions and follow the sequential or rapid decline in gene exchange. PMID:28262789

The self-perceived survival ability and reproductive fitness (SPFit) theory of substance use disorders.

PubMed

Newlin, David B

2002-04-01

A new theory of substance use disorders is proposed-the SPFit theory-that is based on evolutionary biology and adaptive systems. Self-perceived survival ability and reproductive fitness (SPFit) is proposed as a human psychobiological construct that prioritizes and organizes (i.e. motivates) behavior, but is highly vulnerable to temporary, artificial activation by drugs of abuse. Autoshaping/sign-tracking/feature positive phenomena are proposed to underlie the development of craving and expectations about drugs as the individual learns that abused drugs will easily and reliably inflate SPFit. The cortico-mesolimbic dopamine system and its modulating interconnections are viewed as the biological substrate of SPFit; it is proposed to be a survival and reproductive motivation system rather than a reward center or reward pathway. Finally, the concept of modularity of mind is applied to the SPFit construct. Although considerable empirical data are consistent with the theory, new research is needed to test specific hypotheses derived from SPFit theory.

Lima bean (Phaseolus lunatus) seed coat phaseolin is detrimental to the cowpea weevil (Callosobruchus maculatus).

PubMed

Moraes, R A; Sales, M P; Pinto, M S; Silva, L B; Oliveira, A E; Machado, O L; Fernandes, K V; Xavier-Filho, J

2000-02-01

The presence of phaseolin (a vicilin-like 7S storage globulin) peptides in the seed coat of the legume Phaseolus lunatus L. (lima bean) was demonstrated by N-terminal amino acid sequencing. Utilizing an artificial seed system assay we showed that phaseolin, isolated from both cotyledon and testa tissues of P. lunatus, is detrimental to the nonhost bruchid Callosobruchus maculatus (F) (cowpea weevil) with ED50 of 1.7 and 3.5%, respectively. The level of phaseolin in the seed coat (16.7%) was found to be sufficient to deter larval development of this bruchid. The expression of a C. maculatus-detrimental protein in the testa of nonhost seeds suggests that the protein may have played a significant role in the evolutionary adaptation of bruchids to legume seeds.

Opportunity for natural selection among five population groups of Manipur, North East India.

PubMed

Asghar, M; Meitei, S Y; Luxmi, Y; Achoubi, N; Meitei, K S; Murry, B; Sachdeva, M P; Saraswathy, K N

2014-01-01

Opportunity for natural selection among five population groups of Manipur in comparison with other North East Indian population has been studied. Crow's index as well as Johnston and Kensinger's index for natural selection were calculated based on differential fertility and mortality. The mortality component was found to be lower compared to fertility component in all the populations which may attribute to comparatively improved and easily accessible health care facilities. However, different selection pressures, artificial and natural, seem to be influencing the selection intensity through induced abortion and spontaneous abortion among the two non-tribal migrant groups: Bamon and Muslims, respectively. This study highlights the probable interaction of artificial and natural selection in determining the evolutionary fate of any population group.

Unified method of knowledge representation in the evolutionary artificial intelligence systems

NASA Astrophysics Data System (ADS)

Bykov, Nickolay M.; Bykova, Katherina N.

2003-03-01

The evolution of artificial intelligence systems called by complicating of their operation topics and science perfecting has resulted in a diversification of the methods both the algorithms of knowledge representation and usage in these systems. Often by this reason it is very difficult to design the effective methods of knowledge discovering and operation for such systems. In the given activity the authors offer a method of unitized representation of the systems knowledge about objects of an external world by rank transformation of their descriptions, made in the different features spaces: deterministic, probabilistic, fuzzy and other. The proof of a sufficiency of the information about the rank configuration of the object states in the features space for decision making is presented. It is shown that the geometrical and combinatorial model of the rank configurations set introduce their by group of some system of incidence, that allows to store the information on them in a convolute kind. The method of the rank configuration description by the DRP - code (distance rank preserving code) is offered. The problems of its completeness, information capacity, noise immunity and privacy are reviewed. It is shown, that the capacity of a transmission channel for such submission of the information is more than unit, as the code words contain the information both about the object states, and about the distance ranks between them. The effective algorithm of the data clustering for the object states identification, founded on the given code usage, is described. The knowledge representation with the help of the rank configurations allows to unitize and to simplify algorithms of the decision making by fulfillment of logic operations above the DRP - code words. Examples of the proposed clustering techniques operation on the given samples set, the rank configuration of resulted clusters and its DRP-codes are presented.

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.

Evolution of brain region volumes during artificial selection for relative brain size.

PubMed

Kotrschal, Alexander; Zeng, Hong-Li; van der Bijl, Wouter; Öhman-Mägi, Caroline; Kotrschal, Kurt; Pelckmans, Kristiaan; Kolm, Niclas

2017-12-01

The vertebrate brain shows an extremely conserved layout across taxa. Still, the relative sizes of separate brain regions vary markedly between species. One interesting pattern is that larger brains seem associated with increased relative sizes only of certain brain regions, for instance telencephalon and cerebellum. Till now, the evolutionary association between separate brain regions and overall brain size is based on comparative evidence and remains experimentally untested. Here, we test the evolutionary response of brain regions to directional selection on brain size in guppies (Poecilia reticulata) selected for large and small relative brain size. In these animals, artificial selection led to a fast response in relative brain size, while body size remained unchanged. We use microcomputer tomography to investigate how the volumes of 11 main brain regions respond to selection for larger versus smaller brains. We found no differences in relative brain region volumes between large- and small-brained animals and only minor sex-specific variation. Also, selection did not change allometric scaling between brain and brain region sizes. Our results suggest that brain regions respond similarly to strong directional selection on relative brain size, which indicates that brain anatomy variation in contemporary species most likely stem from direct selection on key regions. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Nature vs Nurture: Effects of Learning on Evolution

NASA Astrophysics Data System (ADS)

Nagrani, Nagina

In the field of Evolutionary Robotics, the design, development and application of artificial neural networks as controllers have derived their inspiration from biology. Biologists and artificial intelligence researchers are trying to understand the effects of neural network learning during the lifetime of the individuals on evolution of these individuals by qualitative and quantitative analyses. The conclusion of these analyses can help develop optimized artificial neural networks to perform any given task. The purpose of this thesis is to study the effects of learning on evolution. This has been done by applying Temporal Difference Reinforcement Learning methods to the evolution of Artificial Neural Tissue controller. The controller has been assigned the task to collect resources in a designated area in a simulated environment. The performance of the individuals is measured by the amount of resources collected. A comparison has been made between the results obtained by incorporating learning in evolution and evolution alone. The effects of learning parameters: learning rate, training period, discount rate, and policy on evolution have also been studied. It was observed that learning delays the performance of the evolving individuals over the generations. However, the non zero learning rate throughout the evolution process signifies natural selection preferring individuals possessing plasticity.

A Double-Deck Elevator Group Supervisory Control System with Destination Floor Guidance System Using Genetic Network Programming

NASA Astrophysics Data System (ADS)

Yu, Lu; Zhou, Jin; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor

The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two elevators are connected with each other, serve passengers at two consecutive floors simultaneously. Double-deck Elevator systems (DDES) become more complex in their behavior than conventional single-deck elevator systems (SDES). Recently, Artificial Intelligence (AI) technology has been used in such complex systems. Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator when it performs crossover and mutation operations to judgment nodes and processing nodes. Meanwhile, Destination Floor Guidance System (DFGS) is installed in DDES, so that passengers can also input their destinations at elevator halls. In this paper, we have applied GNP to DDES and compared DFGS with normal systems. The waiting time and traveling time of DFGS are all improved because of getting more information from DFGS. The simulations showed the effectiveness of the double-deck elevators with DFGS in different building traffics.

Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems.

PubMed

Fiuzy, Mohammad; Haddadnia, Javad; Mollania, Nasrin; Hashemian, Maryam; Hassanpour, Kazem

2012-01-01

Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA", which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA). We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer. According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%" on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients. Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples.

Seasonal variation in male alternative reproductive tactics.

PubMed

Monroe, M J; Amundsen, T; Utne-Palm, A C; Mobley, K B

2016-12-01

Genetic parentage analyses reveal considerable diversity in alternative reproductive behaviours (e.g. sneaking) in many taxa. However, little is known about whether these behaviours vary seasonally and between populations. Here, we investigate seasonal variation in male reproductive behaviours in a population of two-spotted gobies (Gobiusculus flavescens) in Norway. Male two-spotted gobies guard nests, attract females and care for fertilized eggs. We collected clutches and nest-guarding males early and late in the breeding season in artificial nests and used microsatellite markers to reconstruct parentage from a subset of offspring from each nest. We hypothesized that mating, reproductive success and sneaking should be more prevalent early in the breeding season when competition for mates among males is predicted to be higher. However, parentage analyses revealed similar values of mating, reproductive success and high frequencies of successful sneaking early (30% of nests) and late (27% of nests) in the season. We also found that multiple females with eggs in the same nest were fertilized by one or more sneaker males, indicating that some males in this population engage in a satellite strategy. We contrast our results to previous work that demonstrates low levels of cuckoldry in a population in Sweden. Our results demonstrate marked stability in both the genetic mating system and male alternative reproductive tactics over the breeding season. However, sneaking rates may vary geographically within a species, likely due to local selection influencing ecological factors encountered at different locations. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Evolutionary origin of insect–Wolbachia nutritional mutualism

PubMed Central

Nikoh, Naruo; Hosokawa, Takahiro; Moriyama, Minoru; Oshima, Kenshiro; Hattori, Masahira; Fukatsu, Takema

2014-01-01

Obligate insect–bacterium nutritional mutualism is among the most sophisticated forms of symbiosis, wherein the host and the symbiont are integrated into a coherent biological entity and unable to survive without the partnership. Originally, however, such obligate symbiotic bacteria must have been derived from free-living bacteria. How highly specialized obligate mutualisms have arisen from less specialized associations is of interest. Here we address this evolutionary issue by focusing on an exceptional insect–Wolbachia nutritional mutualism. Although Wolbachia endosymbionts are ubiquitously found in diverse insects and generally regarded as facultative/parasitic associates for their insect hosts, a Wolbachia strain associated with the bedbug Cimex lectularius, designated as wCle, was shown to be essential for host’s growth and reproduction via provisioning of B vitamins. We determined the 1,250,060-bp genome of wCle, which was generally similar to the genomes of insect-associated facultative Wolbachia strains, except for the presence of an operon encoding the complete biotin synthetic pathway that was acquired via lateral gene transfer presumably from a coinfecting endosymbiont Cardinium or Rickettsia. Nutritional and physiological experiments, in which wCle-infected and wCle-cured bedbugs of the same genetic background were fed on B-vitamin–manipulated blood meals via an artificial feeding system, demonstrated that wCle certainly synthesizes biotin, and the wCle-provisioned biotin significantly contributes to the host fitness. These findings strongly suggest that acquisition of a single gene cluster consisting of biotin synthesis genes underlies the bedbug–Wolbachia nutritional mutualism, uncovering an evolutionary transition from facultative symbiosis to obligate mutualism facilitated by lateral gene transfer in an endosymbiont lineage. PMID:24982177

Natural language processing, pragmatics, and verbal behavior

PubMed Central

Cherpas, Chris

1992-01-01

Natural Language Processing (NLP) is that part of Artificial Intelligence (AI) concerned with endowing computers with verbal and listener repertoires, so that people can interact with them more easily. Most attention has been given to accurately parsing and generating syntactic structures, although NLP researchers are finding ways of handling the semantic content of language as well. It is increasingly apparent that understanding the pragmatic (contextual and consequential) dimension of natural language is critical for producing effective NLP systems. While there are some techniques for applying pragmatics in computer systems, they are piecemeal, crude, and lack an integrated theoretical foundation. Unfortunately, there is little awareness that Skinner's (1957) Verbal Behavior provides an extensive, principled pragmatic analysis of language. The implications of Skinner's functional analysis for NLP and for verbal aspects of epistemology lead to a proposal for a “user expert”—a computer system whose area of expertise is the long-term computer user. The evolutionary nature of behavior suggests an AI technology known as genetic algorithms/programming for implementing such a system. ImagesFig. 1 PMID:22477052

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment.

PubMed

Yao, Yao; Storme, Veronique; Marchal, Kathleen; Van de Peer, Yves

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

PubMed Central

Yao, Yao; Storme, Veronique; Marchal, Kathleen

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population. PMID:28028477

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.

Roboter in der Raumfahrt

NASA Astrophysics Data System (ADS)

Hirzinger, G.

(Robots in space)—The paper emphasizes the enormous automation impact in industry caused by microelectronics, a "byproduct" of space-technology. The evolutionary stages of robotic are outlined and it is shown that there are a lot of reasons for more automation, artificial intelligence and robotic in space, too. The telemanipulator concept is compared with the industrial robot concept, both showing up an increasing degree of similarity. The state of the art in sensory systems is discussed. By hand of the typical operations needed in space as rendezvous, assembly and docking the required robot skill is indicated. As a conclusion it is stated that the basic technologies available with industrial robots today could solve a lot of space problems. What remains to do—apart of course from ongoing research—is better integration and adaption of industrial techniques to the need of space technology.

Intelligent systems technology infrastructure for integrated systems

NASA Technical Reports Server (NTRS)

Lum, Henry, Jr.

1991-01-01

Significant advances have occurred during the last decade in intelligent systems technologies (a.k.a. knowledge-based systems, KBS) including research, feasibility demonstrations, and technology implementations in operational environments. Evaluation and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent systems technologies can be realized for Automated Rendezvous and Capture applications. The successful implementation of these technologies involve a complex system infrastructure integrating the requirements of transportation, vehicle checkout and health management, and communication systems without compromise to systems reliability and performance. The resources that must be invoked to accomplish these tasks include remote ground operations and control, built-in system fault management and control, and intelligent robotics. To ensure long-term evolution and integration of new validated technologies over the lifetime of the vehicle, system interfaces must also be addressed and integrated into the overall system interface requirements. An approach for defining and evaluating the system infrastructures including the testbed currently being used to support the on-going evaluations for the evolutionary Space Station Freedom Data Management System is presented and discussed. Intelligent system technologies discussed include artificial intelligence (real-time replanning and scheduling), high performance computational elements (parallel processors, photonic processors, and neural networks), real-time fault management and control, and system software development tools for rapid prototyping capabilities.

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.

Next gen perception and cognition: augmenting perception and enhancing cognition through mobile technologies

NASA Astrophysics Data System (ADS)

Goma, Sergio R.

2015-03-01

In current times, mobile technologies are ubiquitous and the complexity of problems is continuously increasing. In the context of advancement of engineering, we explore in this paper possible reasons that could cause a saturation in technology evolution - namely the ability of problem solving based on previous results and the ability of expressing solutions in a more efficient way, concluding that `thinking outside of brain' - as in solving engineering problems that are expressed in a virtual media due to their complexity - would benefit from mobile technology augmentation. This could be the necessary evolutionary step that would provide the efficiency required to solve new complex problems (addressing the `running out of time' issue) and remove the communication of results barrier (addressing the human `perception/expression imbalance' issue). Some consequences are discussed, as in this context the artificial intelligence becomes an automation tool aid instead of a necessary next evolutionary step. The paper concludes that research in modeling as problem solving aid and data visualization as perception aid augmented with mobile technologies could be the path to an evolutionary step in advancing engineering.

Uncovering Wolbachia Diversity upon Artificial Host Transfer

PubMed Central

Schneider, Daniela I.; Riegler, Markus; Arthofer, Wolfgang; Merçot, Hervé; Stauffer, Christian; Miller, Wolfgang J.

2013-01-01

The common endosymbiotic Wolbachia bacteria influence arthropod hosts in multiple ways. They are mostly recognized for their manipulations of host reproduction, yet, more recent studies demonstrate that Wolbachia also impact host behavior, metabolic pathways and immunity. Besides their biological and evolutionary roles, Wolbachia are new potential biological control agents for pest and vector management. Importantly, Wolbachia-based control strategies require controlled symbiont transfer between host species and predictable outcomes of novel Wolbachia-host associations. Theoretically, this artificial horizontal transfer could inflict genetic changes within transferred Wolbachia populations. This could be facilitated through de novo mutations in the novel recipient host or changes of haplotype frequencies of polymorphic Wolbachia populations when transferred from donor to recipient hosts. Here we show that Wolbachia resident in the European cherry fruit fly, Rhagoletis cerasi, exhibit ancestral and cryptic sequence polymorphism in three symbiont genes, which are exposed upon microinjection into the new hosts Drosophila simulans and Ceratitis capitata. Our analyses of Wolbachia in microinjected D. simulans over 150 generations after microinjection uncovered infections with multiple Wolbachia strains in trans-infected lines that had previously been typed as single infections. This confirms the persistence of low-titer Wolbachia strains in microinjection experiments that had previously escaped standard detection techniques. Our study demonstrates that infections by multiple Wolbachia strains can shift in prevalence after artificial host transfer driven by either stochastic or selective processes. Trans-infection of Wolbachia can claim fitness costs in new hosts and we speculate that these costs may have driven the shifts of Wolbachia strains that we saw in our model system. PMID:24376534

Uncovering Wolbachia diversity upon artificial host transfer.

PubMed

Schneider, Daniela I; Riegler, Markus; Arthofer, Wolfgang; Merçot, Hervé; Stauffer, Christian; Miller, Wolfgang J

2013-01-01

The common endosymbiotic Wolbachia bacteria influence arthropod hosts in multiple ways. They are mostly recognized for their manipulations of host reproduction, yet, more recent studies demonstrate that Wolbachia also impact host behavior, metabolic pathways and immunity. Besides their biological and evolutionary roles, Wolbachia are new potential biological control agents for pest and vector management. Importantly, Wolbachia-based control strategies require controlled symbiont transfer between host species and predictable outcomes of novel Wolbachia-host associations. Theoretically, this artificial horizontal transfer could inflict genetic changes within transferred Wolbachia populations. This could be facilitated through de novo mutations in the novel recipient host or changes of haplotype frequencies of polymorphic Wolbachia populations when transferred from donor to recipient hosts. Here we show that Wolbachia resident in the European cherry fruit fly, Rhagoletis cerasi, exhibit ancestral and cryptic sequence polymorphism in three symbiont genes, which are exposed upon microinjection into the new hosts Drosophila simulans and Ceratitis capitata. Our analyses of Wolbachia in microinjected D. simulans over 150 generations after microinjection uncovered infections with multiple Wolbachia strains in trans-infected lines that had previously been typed as single infections. This confirms the persistence of low-titer Wolbachia strains in microinjection experiments that had previously escaped standard detection techniques. Our study demonstrates that infections by multiple Wolbachia strains can shift in prevalence after artificial host transfer driven by either stochastic or selective processes. Trans-infection of Wolbachia can claim fitness costs in new hosts and we speculate that these costs may have driven the shifts of Wolbachia strains that we saw in our model system.

Anticipatory Mechanisms in Evolutionary Living Systems

NASA Astrophysics Data System (ADS)

Dubois, Daniel M.; Holmberg, Stig C.

2010-11-01

This paper deals firstly with a revisiting of Darwin's theory of Natural Selection. Darwin in his book never uses the word "evolution", but shows a clear position about mutability of species. Darwin's Natural Selection was mainly inspired by the anticipatory Artificial Selection by humans in domestication, and the Malthus struggle for existence. Darwin showed that the struggle for existence leads to the preservation of the most divergent offspring of any one species. He cited several times the canon of "Natura non facit saltum". He spoke about the origin of life from some one primordial form, into which life was first breathed. Finally, Darwin made anticipation about the future researches in psychology. This paper cites the work of Ernst Mayr who was the first, after 90 years of an intense scientific debate, to present a new and stable Darwinian paradigm as the "Evolutionary Synthesis" in 1942. To explain what is life, the Living Systems Theory (LST) by J. G. Miller is presented. It is showed that the Autopoietic Systems Theory of Varela et al is also a fundamental component of living systems. In agreement with Darwin, the natural selection is a necessary condition for transformation of biological systems, but is not a sufficient condition. Thus, in this paper we conjecture that an anticipatory evolutionary mechanism exists with the genetic code that is a self-replicating and self-modifying anticipatory program. As demonstrated by Nobel laureate McClintock, evolution in genomes is programmed. The word "program" comes from "pro-gram" meaning to write before, by anticipation, and means a plan for the programming of a mechanism, or a sequence of coded instructions that can be inserted into a mechanism, or a sequence of coded instructions, as genes of behavioural responses, that is part of an organism. For example, cell death may be programmed by what is called the apoptosis. This definitively is a great breakthrough in our understanding of biological evolution. Hence, it is possible to formulate a new principle of evolution, i.e. the principle of Double Anticipatory Loop (DAL) of evolution: Biological evolution is driven by interaction between a mindless environment that is passively selecting the fittest inhabitants and purposeful anticipatory living systems, which are actively selecting and creating their own environment. Evolution on the genome level is trigged by environmental stress but guided by an inherent program.

Infomax Strategies for an Optimal Balance Between Exploration and Exploitation

NASA Astrophysics Data System (ADS)

Reddy, Gautam; Celani, Antonio; Vergassola, Massimo

2016-06-01

Proper balance between exploitation and exploration is what makes good decisions that achieve high reward, like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific applications turn out to be successful, the validity of information as a proxy for reward remains unclear. Here, we consider the multi-armed bandit decision problem, which features arms (slot-machines) of unknown probabilities of success and a player trying to maximize cumulative payoff by choosing the sequence of arms to play. We show that an Infomax strategy (Info-p) which optimally gathers information on the highest probability of success among the arms, saturates known optimal bounds and compares favorably to existing policies. Conversely, gathering information on the identity of the best arm in the bandit leads to a strategy that is vastly suboptimal in terms of payoff. The nature of the quantity selected for Infomax acquisition is then crucial for effective tradeoffs between exploration and exploitation.

Heuristic extraction of rules in pruned artificial neural networks models used for quantifying highly overlapping chromatographic peaks.

PubMed

Hervás, César; Silva, Manuel; Serrano, Juan Manuel; Orejuela, Eva

2004-01-01

The suitability of an approach for extracting heuristic rules from trained artificial neural networks (ANNs) pruned by a regularization method and with architectures designed by evolutionary computation for quantifying highly overlapping chromatographic peaks is demonstrated. The ANN input data are estimated by the Levenberg-Marquardt method in the form of a four-parameter Weibull curve associated with the profile of the chromatographic band. To test this approach, two N-methylcarbamate pesticides, carbofuran and propoxur, were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis. Straightforward network topologies (one and two outputs models) allow the analytes to be quantified in concentration ratios ranging from 1:7 to 5:1 with an average standard error of prediction for the generalization test of 2.7 and 2.3% for carbofuran and propoxur, respectively. The reduced dimensions of the selected ANN architectures, especially those obtained after using heuristic rules, allowed simple quantification equations to be developed that transform the input variables into output variables. These equations can be easily interpreted from a chemical point of view to attain quantitative analytical information regarding the effect of both analytes on the characteristics of chromatographic bands, namely profile, dispersion, peak height, and residence time. Copyright 2004 American Chemical Society

The use of surrogates for an optimal management of coupled groundwater-agriculture hydrosystems

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Brettschneider, M.; Schmitz, G. H.; Lennartz, F.

2012-04-01

For ensuring an optimal sustainable water resources management in arid coastal environments, we develop a new simulation based integrated water management system. It aims at achieving best possible solutions for groundwater withdrawals for agricultural and municipal water use including saline water management together with a substantial increase of the water use efficiency in irrigated agriculture. To achieve a robust and fast operation of the management system regarding water quality and water quantity we develop appropriate surrogate models by combining physically based process modelling with methods of artificial intelligence. Thereby we use an artificial neural network for modelling the aquifer response, inclusive the seawater interface, which was trained on a scenario database generated by a numerical density depended groundwater flow model. For simulating the behaviour of high productive agricultural farms crop water production functions are generated by means of soil-vegetation-atmosphere-transport (SVAT)-models, adapted to the regional climate conditions, and a novel evolutionary optimisation algorithm for optimal irrigation scheduling and control. We apply both surrogates exemplarily within a simulation based optimisation environment using the characteristics of the south Batinah region in the Sultanate of Oman which is affected by saltwater intrusion into the coastal aquifer due to excessive groundwater withdrawal for irrigated agriculture. We demonstrate the effectiveness of our methodology for the evaluation and optimisation of different irrigation practices, cropping pattern and resulting abstraction scenarios. Due to contradicting objectives like profit-oriented agriculture vs. aquifer sustainability a multi-criterial optimisation is performed.

Global facial beauty: approaching a unified aesthetic ideal.

PubMed

Sands, Noah B; Adamson, Peter A

2014-04-01

Recognition of facial beauty is both inborn and learned through social discourses and exposures. Demographic shifts across the globe, in addition to cross-cultural interactions that typify 21st century globalization in virtually all industries, comprise major active evolutionary forces that reshape our individual notions of facial beauty. This article highlights the changing perceptions of beauty, while defining and distinguishing natural beauty and artificial beauty. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Evolutionary Artificial Neural Network Weight Tuning to Optimize Decision Making for an Abstract Game

DTIC Science & Technology

2010-03-01

separate LoA heuristic. If any of the examined heuristics produced competitive player , then the final measurement was a success . Barring that, a...if offline training actually results in a successful player . Whereas offline learning plays many games and then trains as many networks as desired...a competitive Lines of Action player , shedding light on the difficulty of developing a neural network to model such a large and complex solution

Complex constraints on allometry revealed by artificial selection on the wing of Drosophila melanogaster

PubMed Central

Bolstad, Geir H.; Cassara, Jason A.; Márquez, Eladio; Hansen, Thomas F.; van der Linde, Kim; Houle, David; Pélabon, Christophe

2015-01-01

Precise exponential scaling with size is a fundamental aspect of phenotypic variation. These allometric power laws are often invariant across taxa and have long been hypothesized to reflect developmental constraints. Here we test this hypothesis by investigating the evolutionary potential of an allometric scaling relationship in drosophilid wing shape that is nearly invariant across 111 species separated by at least 50 million years of evolution. In only 26 generations of artificial selection in a population of Drosophila melanogaster, we were able to drive the allometric slope to the outer range of those found among the 111 sampled species. This response was rapidly lost when selection was suspended. Only a small proportion of this reversal could be explained by breakup of linkage disequilibrium, and direct selection on wing shape is also unlikely to explain the reversal, because the more divergent wing shapes produced by selection on the allometric intercept did not revert. We hypothesize that the reversal was instead caused by internal selection arising from pleiotropic links to unknown traits. Our results also suggest that the observed selection response in the allometric slope was due to a component expressed late in larval development and that variation in earlier development did not respond to selection. Together, these results are consistent with a role for pleiotropic constraints in explaining the remarkable evolutionary stability of allometric scaling. PMID:26371319

Does Sex Trade with Violence among Genotypes in Drosophila melanogaster?

PubMed Central

Cabral, Larry G.; Foley, Brad R.; Nuzhdin, Sergey V.

2008-01-01

The evolutionary forces shaping the ability to win competitive interactions, such as aggressive encounters, are still poorly understood. Given a fitness advantage for competitive success, variance in aggressive and sexual display traits should be depleted, but a great deal of variation in these traits is consistently found. While life history tradeoffs have been commonly cited as a mechanism for the maintenance of variation, the variability of competing strategies of conspecifics may mean there is no single optimum strategy. We measured the genetically determined outcomes of aggressive interactions, and the resulting effects on mating success, in a panel of diverse inbred lines representing both natural variation and artificially selected genotypes. Males of one genotype which consistently lost territorial encounters with other genotypes were nonetheless successful against males that were artificially selected for supernormal aggression and dominated all other lines. Intransitive patterns of territorial success could maintain variation in aggressive strategies if there is a preference for territorial males. Territorial success was not always associated with male mating success however and females preferred ‘winners’ among some male genotypes, and ‘losers’ among other male genotypes. This suggests that studying behaviour from the perspective of population means may provide limited evolutionary and genetic insight. Overall patterns of competitive success among males and mating transactions between the sexes are consistent with mechanisms proposed for the maintenance of genetic variation due to nonlinear outcomes of competitive interactions. PMID:18414669

Does sex trade with violence among genotypes in Drosophila melanogaster?

PubMed

Cabral, Larry G; Foley, Brad R; Nuzhdin, Sergey V

2008-04-16

The evolutionary forces shaping the ability to win competitive interactions, such as aggressive encounters, are still poorly understood. Given a fitness advantage for competitive success, variance in aggressive and sexual display traits should be depleted, but a great deal of variation in these traits is consistently found. While life history tradeoffs have been commonly cited as a mechanism for the maintenance of variation, the variability of competing strategies of conspecifics may mean there is no single optimum strategy. We measured the genetically determined outcomes of aggressive interactions, and the resulting effects on mating success, in a panel of diverse inbred lines representing both natural variation and artificially selected genotypes. Males of one genotype which consistently lost territorial encounters with other genotypes were nonetheless successful against males that were artificially selected for supernormal aggression and dominated all other lines. Intransitive patterns of territorial success could maintain variation in aggressive strategies if there is a preference for territorial males. Territorial success was not always associated with male mating success however and females preferred 'winners' among some male genotypes, and 'losers' among other male genotypes. This suggests that studying behaviour from the perspective of population means may provide limited evolutionary and genetic insight. Overall patterns of competitive success among males and mating transactions between the sexes are consistent with mechanisms proposed for the maintenance of genetic variation due to nonlinear outcomes of competitive interactions.

Atypical birdsong and artificial languages provide insights into how communication systems are shaped by learning, use, and transmission.

PubMed

Fehér, Olga

2017-02-01

In this article, I argue that a comparative approach focusing on the cognitive capacities and behavioral mechanisms that underlie vocal learning in songbirds and humans can provide valuable insights into the evolutionary origins of language. The experimental approaches I discuss use abnormal song and atypical linguistic input to study the processes of individual learning, social interaction, and cultural transmission. Atypical input places increased learning and communicative pressure on learners, so exploring how they respond to this type of input provides a particularly clear picture of the biases and constraints at work during learning and use. Furthermore, simulating the cultural transmission of these unnatural communication systems in the laboratory informs us about how learning and social biases influence the structure of communication systems in the long run. Findings based on these methods suggest fundamental similarities in the basic social-cognitive mechanisms underlying vocal learning in birds and humans, and continuing research promises insights into the uniquely human mechanisms and into how human cognition and social behavior interact, and ultimately impact on the evolution of language.

Domestication and fitness in the wild: A multivariate view.

PubMed

Tufto, Jarle

2017-09-01

Domesticated species continually escaping and interbreeding with wild relatives impose a migration load on wild populations. As domesticated stocks become increasingly different as a result of artificial and natural selection in captivity, fitness of escapees in the wild is expected to decline, reducing the effective rate of migration into wild populations. Recent theory suggest that this may alleviate and eventually eliminate the resulting migration load. I develop a multivariate model of trait and wild fitness evolution resulting from the joint effects of artificial and natural selection in the captive environment. Initially, the evolutionary trajectory is dominated by the effects of artificial selection causing a fast initial decline in fitness of escapees in the wild. In later phases, through the counteracting effects of correlational multivariate natural selection in captivity, the mean phenotype is pushed in directions of weak stabilizing selection, allowing a sustained response in the trait subject to artificial selection. Provided that there is some alignment between the adaptive landscapes in the wild and in captivity, these phases are associated with slower rates of decline in wild fitness of the domesticated stock, suggesting that detrimental effects on wild populations are likely to remain a concern in the foreseeable future. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

From wild animals to domestic pets, an evolutionary view of domestication.

PubMed

Driscoll, Carlos A; Macdonald, David W; O'Brien, Stephen J

2009-06-16

Artificial selection is the selection of advantageous natural variation for human ends and is the mechanism by which most domestic species evolved. Most domesticates have their origin in one of a few historic centers of domestication as farm animals. Two notable exceptions are cats and dogs. Wolf domestication was initiated late in the Mesolithic when humans were nomadic hunter-gatherers. Those wolves less afraid of humans scavenged nomadic hunting camps and over time developed utility, initially as guards warning of approaching animals or other nomadic bands and soon thereafter as hunters, an attribute tuned by artificial selection. The first domestic cats had limited utility and initiated their domestication among the earliest agricultural Neolithic settlements in the Near East. Wildcat domestication occurred through a self-selective process in which behavioral reproductive isolation evolved as a correlated character of assortative mating coupled to habitat choice for urban environments. Eurasian wildcats initiated domestication and their evolution to companion animals was initially a process of natural, rather than artificial, selection over time driven during their sympatry with forbear wildcats.

Evolution of abandoned channels: Insights on controlling factors in a multi-pressure river system

NASA Astrophysics Data System (ADS)

Dépret, Thomas; Riquier, Jérémie; Piégay, Hervé

2017-10-01

In the second half of the 19th century, channelization of large multi-thread rivers such as the Rhine, the Danube, and the Rhône rivers induced artificial disconnection of most of their secondary channels. Compared to naturally abandoned channels, terrestrialization (i.e., the passage from the aquatic to the terrestrial stage, controlled by sediment deposits and/or lowering of the water level) patterns and rates of such artificially prematurely abandoned channels remain largely unknown. Moreover, factors controlling their evolutionary trajectories are complex owing to a set of pressures occurring throughout the 20th century within specific space-time windows. Through a case study of the Rhône River, this paper aims to assess and distinguish the effects of a set of potential controlling factors on abandoned channel terrestrialization dynamics and lifespan. We tested the influence of: (i) submersible embankments closing the entrance of abandoned channels, (ii) main channel degradation following its channelization or the water level lowering due to channel bypassing in the middle of the 20th century involving drastic water abstraction in these reaches, (iii) transverse dykes within the abandoned channels, (iv) the flooding regime of abandoned channels (i.e., frequency and magnitude of upstream connections producing lotic functioning), and (v) longitudinal variation in the suspended sediment concentration along the main channel. To this end, we studied 24 abandoned channels (16 artificially disconnected at their upstream end by submersible embankments and eight naturally disconnected by bar plug establishment) from the mid-19th to the beginning of the 20th century. Their terrestrialization rates were characterized through the reconstruction of their planimetric trajectories using historical maps and aerial photos. The results reveal a much longer lifespan of artificial abandoned channels compared to natural ones because of the truncation of the initial bedload infilling phase due to the artificial and imposed closing of their entrance. Moreover, terrestrialization occurred faster when water level lowering or channel degradation was greater. Surprisingly, terrestrialization rates were the highest in the most frequently connected artificial abandoned channels (i.e., channels with a high frequency of lotic functioning), probably in relation to the roughness induced by the presence of transversal dykes. Finally, it is difficult to rank all the factors tested because of their complex combinations, which can change in space and time.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

PubMed

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

PubMed Central

2011-01-01

Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology. PMID:21989196

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.

Expanding Evolutionary Theory beyond Darwinism with Elaborating, Self-Organizing, and Fractionating Complex Evolutionary Systems

ERIC Educational Resources Information Center

Fichter, Lynn S.; Pyle, E. J.; Whitmeyer, S. J.

2010-01-01

Earth systems increase in complexity, diversity, and interconnectedness with time, driven by tectonic/solar energy that keeps the systems far from equilibrium. The evolution of Earth systems is facilitated by three evolutionary mechanisms: "elaboration," "fractionation," and "self-organization," that share…

Laying date, incubation and egg breakage as determinants of bacterial load on bird eggshells: experimental evidence.

PubMed

Soler, Juan José; Ruiz-Rodríguez, Magdalena; Martín-Vivaldi, Manuel; Peralta-Sánchez, Juan Manuel; Ruiz-Castellano, Cristina; Tomás, Gustavo

2015-09-01

Exploring factors guiding interactions of bacterial communities with animals has become of primary importance for ecologists and evolutionary biologists during the last years because of their likely central role in the evolution of animal life history traits. We explored the association between laying date and eggshell bacterial load (mesophilic bacteria, Enterobacteriaceae, Staphylococci, and Enterococci) in natural and artificial magpie (Pica pica) nests containing fresh commercial quail (Coturnix coturnix) eggs. We manipulated hygiene conditions by spilling egg contents on magpie and artificial nests and explored experimental effects during the breeding season. Egg breakage is a common outcome of brood parasitism by great spotted cuckoos (Clamator glandarius) on the nests of magpie, one of its main hosts. We found that the treatment increased eggshell bacterial load in artificial nests, but not in magpie nests with incubating females, which suggests that parental activity prevents the proliferation of bacteria on the eggshells in relation to egg breakage. Moreover, laying date was positively related to eggshell bacterial load in active magpie nests, but negatively in artificial nests. The results suggest that variation in parental characteristics of magpies rather than climatic variation during the breeding season explained the detected positive association. Because the eggshell bacterial load is a proxy of hatching success, the detected positive association between eggshell bacterial loads and laying date in natural, but not in artificial nests, suggests that the generalized negative association between laying date and avian breeding success can be, at least partially, explained by differential bacterial effects.

Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

PubMed

González-Álvarez, Julio; Cervera-Crespo, Teresa

2018-01-01

Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series

NASA Astrophysics Data System (ADS)

McKinney, B. A.; Crowe, J. E., Jr.; Voss, H. U.; Crooke, P. S.; Barney, N.; Moore, J. H.

2006-02-01

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual’s response to the smallpox vaccine.

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

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/

Sheep genome functional annotation reveals proximal regulatory elements contributed to the evolution of modern breeds.

PubMed

Naval-Sanchez, Marina; Nguyen, Quan; McWilliam, Sean; Porto-Neto, Laercio R; Tellam, Ross; Vuocolo, Tony; Reverter, Antonio; Perez-Enciso, Miguel; Brauning, Rudiger; Clarke, Shannon; McCulloch, Alan; Zamani, Wahid; Naderi, Saeid; Rezaei, Hamid Reza; Pompanon, Francois; Taberlet, Pierre; Worley, Kim C; Gibbs, Richard A; Muzny, Donna M; Jhangiani, Shalini N; Cockett, Noelle; Daetwyler, Hans; Kijas, James

2018-02-28

Domestication fundamentally reshaped animal morphology, physiology and behaviour, offering the opportunity to investigate the molecular processes driving evolutionary change. Here we assess sheep domestication and artificial selection by comparing genome sequence from 43 modern breeds (Ovis aries) and their Asian mouflon ancestor (O. orientalis) to identify selection sweeps. Next, we provide a comparative functional annotation of the sheep genome, validated using experimental ChIP-Seq of sheep tissue. Using these annotations, we evaluate the impact of selection and domestication on regulatory sequences and find that sweeps are significantly enriched for protein coding genes, proximal regulatory elements of genes and genome features associated with active transcription. Finally, we find individual sites displaying strong allele frequency divergence are enriched for the same regulatory features. Our data demonstrate that remodelling of gene expression is likely to have been one of the evolutionary forces that drove phenotypic diversification of this common livestock species.

Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems

PubMed Central

Fiuzy, Mohammad; Haddadnia, Javad; Mollania, Nasrin; Hashemian, Maryam; Hassanpour, Kazem

2012-01-01

Background Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA”, which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA). Methods We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer. Results According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%” on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients. Conclusion Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. PMID:25352966

Using 3D printed eggs to examine the egg-rejection behaviour of wild birds

PubMed Central

Nunez, Valerie; Voss, Henning U.; Croston, Rebecca; Aidala, Zachary; López, Analía V.; Van Tatenhove, Aimee; Holford, Mandë E.; Shawkey, Matthew D.; Hauber, Mark E.

2015-01-01

The coevolutionary relationships between brood parasites and their hosts are often studied by examining the egg rejection behaviour of host species using artificial eggs. However, the traditional methods for producing artificial eggs out of plasticine, plastic, wood, or plaster-of-Paris are laborious, imprecise, and prone to human error. As an alternative, 3D printing may reduce human error, enable more precise manipulation of egg size and shape, and provide a more accurate and replicable protocol for generating artificial stimuli than traditional methods. However, the usefulness of 3D printing technology for egg rejection research remains to be tested. Here, we applied 3D printing technology to the extensively studied egg rejection behaviour of American robins, Turdus migratorius. Eggs of the robin’s brood parasites, brown-headed cowbirds, Molothrus ater, vary greatly in size and shape, but it is unknown whether host egg rejection decisions differ across this gradient of natural variation. We printed artificial eggs that encompass the natural range of shapes and sizes of cowbird eggs, painted them to resemble either robin or cowbird egg colour, and used them to artificially parasitize nests of breeding wild robins. In line with previous studies, we show that robins accept mimetically coloured and reject non-mimetically coloured artificial eggs. Although we found no evidence that subtle differences in parasitic egg size or shape affect robins’ rejection decisions, 3D printing will provide an opportunity for more extensive experimentation on the potential biological or evolutionary significance of size and shape variation of foreign eggs in rejection decisions. We provide a detailed protocol for generating 3D printed eggs using either personal 3D printers or commercial printing services, and highlight additional potential future applications for this technology in the study of egg rejection. PMID:26038720

Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

2016-09-01

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

Structural Genomics: Correlation Blocks, Population Structure, and Genome Architecture

PubMed Central

Hu, Xin-Sheng; Yeh, Francis C.; Wang, Zhiquan

2011-01-01

An integration of the pattern of genome-wide inter-site associations with evolutionary forces is important for gaining insights into the genomic evolution in natural or artificial populations. Here, we assess the inter-site correlation blocks and their distributions along chromosomes. A correlation block is broadly termed as the DNA segment within which strong correlations exist between genetic diversities at any two sites. We bring together the population genetic structure and the genomic diversity structure that have been independently built on different scales and synthesize the existing theories and methods for characterizing genomic structure at the population level. We discuss how population structure could shape correlation blocks and their patterns within and between populations. Effects of evolutionary forces (selection, migration, genetic drift, and mutation) on the pattern of genome-wide correlation blocks are discussed. In eukaryote organisms, we briefly discuss the associations between the pattern of correlation blocks and genome assembly features in eukaryote organisms, including the impacts of multigene family, the perturbation of transposable elements, and the repetitive nongenic sequences and GC-rich isochores. Our reviews suggest that the observable pattern of correlation blocks can refine our understanding of the ecological and evolutionary processes underlying the genomic evolution at the population level. PMID:21886455

Does selection on increased cold tolerance in the adult stage confer resistance throughout development?

PubMed

Dierks, A; Kölzow, N; Franke, K; Fischer, K

2012-08-01

Artificial selection is a powerful approach to unravel constraints on genetic adaptation. Although it has been frequently used to reveal genetic trade-offs among different fitness-related traits, only a few studies have targeted genetic correlations across developmental stages. Here, we test whether selection on increased cold tolerance in the adult stage increases cold resistance throughout ontogeny in the butterfly Bicyclus anynana. We used lines selected for decreased chill-coma recovery time and corresponding controls, which had originally been set up from three levels of inbreeding (outbred control, one or two full-sib matings). Four generations after having terminated selection, a response to selection was found in 1-day-old butterflies (the age at which selection took place). Older adults showed a very similar although weaker response. Nevertheless, cold resistance did not increase in either egg, larval or pupal stage in the selection lines but was even lower compared to control lines for eggs and young larvae. These findings suggest a cost of increased adult cold tolerance, presumably reducing resource availability for offspring provisioning and thereby stress tolerance during development, which may substantially affect evolutionary trajectories. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

Functional genomics of the evolution of increased resistance to parasitism in Drosophila.

PubMed

Wertheim, Bregje; Kraaijeveld, Alex R; Hopkins, Meirion G; Walther Boer, Mark; Godfray, H Charles J

2011-03-01

Individual hosts normally respond to parasite attack by launching an acute immune response (a phenotypic plastic response), while host populations can respond in the longer term by evolving higher level of defence against parasites. Little is known about the genetics of the evolved response: the identity and number of genes involved and whether it involves a pre-activation of the regulatory systems governing the plastic response. We explored these questions by surveying transcriptional changes in a Drosophila melanogaster strain artificially selected for resistance against the hymenopteran endoparasitoid Asobara tabida. Using micro-arrays, we profiled gene expression at seven time points during development (from the egg to the second instar larva) and found a large number of genes (almost 900) with altered expression levels. Bioinformatic analysis showed that some were involved in immunity or defence-associated functions but many were not. Previously, we had defined a set of genes whose level of expression changed after parasitoid attack and a comparison with the present set showed a significant though comparatively small overlap. This suggests that the evolutionary response to parasitism is not a simple pre-activation of the plastic, acute response. We also found overlap in the genes involved in the evolutionary response to parasitism and to other biotic and abiotic stressors, perhaps suggesting a 'module' of genes involved in a generalized stress response as has been found in other organisms. © 2010 Blackwell Publishing Ltd.

The Evolutionary Origins of Hierarchy

PubMed Central

Huizinga, Joost; Clune, Jeff

2016-01-01

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics. PMID:27280881

Differences in the Metabolic Rates of Exploited and Unexploited Fish Populations: A Signature of Recreational Fisheries Induced Evolution?

PubMed Central

Hessenauer, Jan-Michael; Vokoun, Jason C.; Suski, Cory D.; Davis, Justin; Jacobs, Robert; O’Donnell, Eileen

2015-01-01

Non-random mortality associated with commercial and recreational fisheries have the potential to cause evolutionary changes in fish populations. Inland recreational fisheries offer unique opportunities for the study of fisheries induced evolution due to the ability to replicate study systems, limited gene flow among populations, and the existence of unexploited reference populations. Experimental research has demonstrated that angling vulnerability is heritable in Largemouth Bass Micropterus salmoides, and is correlated with elevated resting metabolic rates (RMR) and higher fitness. However, whether such differences are present in wild populations is unclear. This study sought to quantify differences in RMR among replicated exploited and unexploited populations of Largemouth Bass. We collected age-0 Largemouth Bass from two Connecticut drinking water reservoirs unexploited by anglers for almost a century, and two exploited lakes, then transported and reared them in the same pond. Field RMR of individuals from each population was quantified using intermittent-flow respirometry. Individuals from unexploited reservoirs had a significantly higher mean RMR (6%) than individuals from exploited populations. These findings are consistent with expectations derived from artificial selection by angling on Largemouth Bass, suggesting that recreational angling may act as an evolutionary force influencing the metabolic rates of fishes in the wild. Reduced RMR as a result of fisheries induced evolution may have ecosystem level effects on energy demand, and be common in exploited recreational populations globally. PMID:26039091

The Evolutionary Origins of Hierarchy.

PubMed

Mengistu, Henok; Huizinga, Joost; Mouret, Jean-Baptiste; Clune, Jeff

2016-06-01

Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

NASA Astrophysics Data System (ADS)

Li, Dongni; Guo, Rongtao; Zhan, Rongxin; Yin, Yong

2018-06-01

In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima.

Elements of an algorithm for optimizing a parameter-structural neural network

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2016-06-01

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination

NASA Astrophysics Data System (ADS)

Rocha, José Celso; Passalia, Felipe José; Matos, Felipe Delestro; Takahashi, Maria Beatriz; Maserati, Marc Peter, Jr.; Alves, Mayra Fernanda; de Almeida, Tamie Guibu; Cardoso, Bruna Lopes; Basso, Andrea Cristina; Nogueira, Marcelo Fábio Gouveia

2017-12-01

There is currently no objective, real-time and non-invasive method for evaluating the quality of mammalian embryos. In this study, we processed images of in vitro produced bovine blastocysts to obtain a deeper comprehension of the embryonic morphological aspects that are related to the standard evaluation of blastocysts. Information was extracted from 482 digital images of blastocysts. The resulting imaging data were individually evaluated by three experienced embryologists who graded their quality. To avoid evaluation bias, each image was related to the modal value of the evaluations. Automated image processing produced 36 quantitative variables for each image. The images, the modal and individual quality grades, and the variables extracted could potentially be used in the development of artificial intelligence techniques (e.g., evolutionary algorithms and artificial neural networks), multivariate modelling and the study of defined structures of the whole blastocyst.

Evolutionary dynamics from a variational principle.

PubMed

Klimek, Peter; Thurner, Stefan; Hanel, Rudolf

2010-07-01

We demonstrate with a thought experiment that fitness-based population dynamical approaches to evolution are not able to make quantitative, falsifiable predictions about the long-term behavior of some evolutionary systems. A key characteristic of evolutionary systems is the ongoing endogenous production of new species. These novel entities change the conditions for already existing species. Even Darwin's Demon, a hypothetical entity with exact knowledge of the abundance of all species and their fitness functions at a given time, could not prestate the impact of these novelties on established populations. We argue that fitness is always a posteriori knowledge--it measures but does not explain why a species has reproductive success or not. To overcome these conceptual limitations, a variational principle is proposed in a spin-model-like setup of evolutionary systems. We derive a functional which is minimized under the most general evolutionary formulation of a dynamical system, i.e., evolutionary trajectories causally emerge as a minimization of a functional. This functional allows the derivation of analytic solutions of the asymptotic diversity for stochastic evolutionary systems within a mean-field approximation. We test these approximations by numerical simulations of the corresponding model and find good agreement in the position of phase transitions in diversity curves. The model is further able to reproduce stylized facts of timeseries from several man-made and natural evolutionary systems. Light will be thrown on how species and their fitness landscapes dynamically coevolve.

Combining genetic and evolutionary engineering to establish C4 metabolism in C3 plants.

PubMed

Li, Yuanyuan; Heckmann, David; Lercher, Martin J; Maurino, Veronica G

2017-01-01

To feed a world population projected to reach 9 billion people by 2050, the productivity of major crops must be increased by at least 50%. One potential route to boost the productivity of cereals is to equip them genetically with the 'supercharged' C 4 type of photosynthesis; however, the necessary genetic modifications are not sufficiently understood for the corresponding genetic engineering programme. In this opinion paper, we discuss a strategy to solve this problem by developing a new paradigm for plant breeding. We propose combining the bioengineering of well-understood traits with subsequent evolutionary engineering, i.e. mutagenesis and artificial selection. An existing mathematical model of C 3 -C 4 evolution is used to choose the most promising path towards this goal. Based on biomathematical simulations, we engineer Arabidopsis thaliana plants that express the central carbon-fixing enzyme Rubisco only in bundle sheath cells (Ru-BSC plants), the localization characteristic for C 4 plants. This modification will initially be deleterious, forcing the Ru-BSC plants into a fitness valley from where previously inaccessible adaptive steps towards C 4 photosynthesis become accessible through fitness-enhancing mutations. Mutagenized Ru-BSC plants are then screened for improved photosynthesis, and are expected to respond to imposed artificial selection pressures by evolving towards C 4 anatomy and biochemistry. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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.

Development of X-TOOLSS: Preliminary Design of Space Systems Using Evolutionary Computation

NASA Technical Reports Server (NTRS)

Schnell, Andrew R.; Hull, Patrick V.; Turner, Mike L.; Dozier, Gerry; Alverson, Lauren; Garrett, Aaron; Reneau, Jarred

2008-01-01

Evolutionary computational (EC) techniques such as genetic algorithms (GA) have been identified as promising methods to explore the design space of mechanical and electrical systems at the earliest stages of design. In this paper the authors summarize their research in the use of evolutionary computation to develop preliminary designs for various space systems. An evolutionary computational solver developed over the course of the research, X-TOOLSS (Exploration Toolset for the Optimization of Launch and Space Systems) is discussed. With the success of early, low-fidelity example problems, an outline of work involving more computationally complex models is discussed.

Artificial Intelligence and Information Retrieval.

ERIC Educational Resources Information Center

Teodorescu, Ioana

1987-01-01

Compares artificial intelligence and information retrieval paradigms for natural language understanding, reviews progress to date, and outlines the applicability of artificial intelligence to question answering systems. A list of principal artificial intelligence software for database front end systems is appended. (CLB)

Computational Intelligence‐Assisted Understanding of Nature‐Inspired Superhydrophobic Behavior

PubMed Central

Zhang, Xia; Ding, Bei; Dixon, Sebastian C.

2017-01-01

Abstract In recent years, state‐of‐the‐art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature‐inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials. PMID:29375975

Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior.

PubMed

Zhang, Xia; Ding, Bei; Cheng, Ran; Dixon, Sebastian C; Lu, Yao

2018-01-01

In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials.

Evolution-based Virtual Content Insertion with Visually Virtual Interactions in Videos

NASA Astrophysics Data System (ADS)

Chang, Chia-Hu; Wu, Ja-Ling

With the development of content-based multimedia analysis, virtual content insertion has been widely used and studied for video enrichment and multimedia advertising. However, how to automatically insert a user-selected virtual content into personal videos in a less-intrusive manner, with an attractive representation, is a challenging problem. In this chapter, we present an evolution-based virtual content insertion system which can insert virtual contents into videos with evolved animations according to predefined behaviors emulating the characteristics of evolutionary biology. The videos are considered not only as carriers of message conveyed by the virtual content but also as the environment in which the lifelike virtual contents live. Thus, the inserted virtual content will be affected by the videos to trigger a series of artificial evolutions and evolve its appearances and behaviors while interacting with video contents. By inserting virtual contents into videos through the system, users can easily create entertaining storylines and turn their personal videos into visually appealing ones. In addition, it would bring a new opportunity to increase the advertising revenue for video assets of the media industry and online video-sharing websites.

Correlated evolution of personality, morphology and performance

PubMed Central

Kern, Elizabeth M. A.; Robinson, Detric; Gass, Erika; Godwin, John; Langerhans, R. Brian

2018-01-01

Evolutionary change in one trait can elicit evolutionary changes in other traits due to genetic correlations. This constrains the independent evolution of traits and can lead to unpredicted ecological and evolutionary outcomes. Animals might frequently exhibit genetic associations among behavioural and morphological-physiological traits, because the physiological mechanisms behind animal personality can have broad multitrait effects and because many selective agents influence the evolution of multiple types of traits. However, we currently know little about genetic correlations between animal personalities and nonbehavioural traits. We tested for associations between personality, morphology and locomotor performance by comparing zebrafish (Danio rerio) collected from the wild and then selectively bred for either a proactive or reactive stress coping style (‘bold’ or ‘shy’ phenotypes). Based on adaptive hypotheses of correlational selection in the wild, we predicted that artificial selection for boldness would produce correlated evolutionary responses of larger caudal regions and higher fast-start escape performance (and the opposite for shyness). After four to seven generations, morphology and locomotor performance differed between personality lines: bold zebrafish exhibited a larger caudal region and higher fast-start performance than fish in the shy line, matching predictions. Individual-level phenotypic correlations suggested that pleiotropy or physical gene linkage likely explained the correlated response of locomotor performance, while the correlated response of body shape may have reflected linkage disequilibrium, which is breaking down each generation in the laboratory. Our results indicate that evolution of personality can result in concomitant changes in morphology and whole-organism performance, and vice versa. PMID:29398712

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

The evolution of resistance genes in multi-protein plant resistance systems.

PubMed

Friedman, Aaron R; Baker, Barbara J

2007-12-01

The genomic perspective aids in integrating the analysis of single resistance (R-) genes into a higher order model of complex plant resistance systems. The majority of R-genes encode a class of proteins with nucleotide binding (NB) and leucine-rich repeat (LRR) domains. Several R-proteins act in multi-protein R-complexes that mediate interaction with pathogen effectors to induce resistance signaling. The complexity of these systems seems to have resulted from multiple rounds of plant-pathogen co-evolution. R-gene evolution is thought to be facilitated by the formation of R-gene clusters, which permit sequence exchanges via recombinatorial mispairing and generate high haplotypic diversity. This pattern of evolution may also generate diversity at other loci that contribute to the R-complex. The rate of recombination at R-clusters is not necessarily homogeneous or consistent over evolutionary time: recent evidence suggests that recombination at R-clusters is increased following pathogen infection, suggesting a mechanism that induces temporary genome instability in response to extreme stress. DNA methylation and chromatin modifications may allow this instability to be conditionally regulated and targeted to specific genome regions. Knowledge of natural R-gene evolution may contribute to strategies for artificial evolution of novel resistance specificities.

Task-level robot programming: Integral part of evolution from teleoperation to autonomy

NASA Technical Reports Server (NTRS)

Reynolds, James C.

1987-01-01

An explanation is presented of task-level robot programming and of how it differs from the usual interpretation of task planning for robotics. Most importantly, it is argued that the physical and mathematical basis of task-level robot programming provides inherently greater reliability than efforts to apply better known concepts from artificial intelligence (AI) to autonomous robotics. Finally, an architecture is presented that allows the integration of task-level robot programming within an evolutionary, redundant, and multi-modal framework that spans teleoperation to autonomy.

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.

Nature versus design: synthetic biology or how to build a biological non-machine.

PubMed

Porcar, M; Peretó, J

2016-04-18

The engineering ideal of synthetic biology presupposes that organisms are composed of standard, interchangeable parts with a predictive behaviour. In one word, organisms are literally recognized as machines. Yet living objects are the result of evolutionary processes without any purposiveness, not of a design by external agents. Biological components show massive overlapping and functional degeneracy, standard-free complexity, intrinsic variation and context dependent performances. However, although organisms are not full-fledged machines, synthetic biologists may still be eager for machine-like behaviours from artificially modified biosystems.

[The application and development of artificial intelligence in medical diagnosis systems].

PubMed

Chen, Zhencheng; Jiang, Yong; Xu, Mingyu; Wang, Hongyan; Jiang, Dazong

2002-09-01

This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Energy-efficient lighting system for television

DOEpatents

Cawthorne, Duane C.

1987-07-21

A light control system for a television camera comprises an artificial light control system which is cooperative with an iris control system. This artificial light control system adjusts the power to lamps illuminating the camera viewing area to provide only sufficient artificial illumination necessary to provide a sufficient video signal when the camera iris is substantially open.

Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.

PubMed

Watson, Richard A; Mills, Rob; Buckley, C L; Kouvaris, Kostas; Jackson, Adam; Powers, Simon T; Cox, Chris; Tudge, Simon; Davies, Adam; Kounios, Loizos; Power, Daniel

2016-01-01

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term "evolutionary connectionism" to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions.

[Review of wireless energy transmission system for total artificial heart].

PubMed

Zhang, Chi; Yang, Ming

2009-11-01

This paper sums up the fundamental structure of wireless energy transmission system for total artificial heart, and compares the key parameters and performance of some representative systems. After that, it is discussed that the future development trend of wireless energy transmission system for total artificial heart.

Enhanced production of medicinal polysaccharide by submerged fermentation of Lingzhi or Reishi medicinal mushroom Ganoderma lucidium (W.Curt.:Fr.) P. Karst. Using statistical and evolutionary optimization methods.

PubMed

Baskar, Gurunathan; Sathya, Shree Rajesh K

2011-01-01

Statistical and evolutionary optimization of media composition was employed for the production of medicinal exopolysaccharide (EPS) by Lingzhi or Reishi medicinal mushroom Ganoderma lucidium MTCC 1039 using soya bean meal flour as low-cost substrate. Soya bean meal flour, ammonium chloride, glucose, and pH were identified as the most important variables for EPS yield using the two-level Plackett-Burman design and further optimized using the central composite design (CCD) and the artificial neural network (ANN)-linked genetic algorithm (GA). The high value of coefficient of determination of ANN (R² = 0.982) indicates that the ANN model was more accurate than the second-order polynomial model of CCD (R² = 0.91) for representing the effect of media composition on EPS yield. The predicted optimum media composition using ANN-linked GA was soybean meal flour 2.98%, glucose 3.26%, ammonium chloride 0.25%, and initial pH 7.5 for the maximum predicted EPS yield of 1005.55 mg/L. The experimental EPS yield obtained using the predicted optimum media composition was 1012.36 mg/L, which validates the high degree of accuracy of evolutionary optimization for enhanced production of EPS by submerged fermentation of G. lucidium.

Current selection for lower migratory activity will drive the evolution of residency in a migratory bird population.

PubMed

Pulido, Francisco; Berthold, Peter

2010-04-20

Global warming is impacting biodiversity by altering the distribution, abundance, and phenology of a wide range of animal and plant species. One of the best documented responses to recent climate change is alterations in the migratory behavior of birds, but the mechanisms underlying these phenotypic adjustments are largely unknown. This knowledge is still crucial to predict whether populations of migratory birds will adapt to a rapid increase in temperature. We monitored migratory behavior in a population of blackcaps (Sylvia atricapilla) to test for evolutionary responses to recent climate change. Using a common garden experiment in time and captive breeding we demonstrated a genetic reduction in migratory activity and evolutionary change in phenotypic plasticity of migration onset. An artificial selection experiment further revealed that residency will rapidly evolve in completely migratory bird populations if selection for shorter migration distance persists. Our findings suggest that current alterations of the environment are favoring birds wintering closer to the breeding grounds and that populations of migratory birds have strongly responded to these changes in selection. The reduction of migratory activity is probably an important evolutionary process in the adaptation of migratory birds to climate change, because it reduces migration costs and facilitates the rapid adjustment to the shifts in the timing of food availability during reproduction.

Influence of the Mechanical Properties of Third-Generation Artificial Turf Systems on Soccer Players’ Physiological and Physical Performance and Their Perceptions

PubMed Central

Sánchez-Sánchez, Javier; García-Unanue, Jorge; Jiménez-Reyes, Pedro; Gallardo, Ana; Burillo, Pablo; Felipe, José Luis; Gallardo, Leonor

2014-01-01

The aim of this research was to evaluate the influence of the mechanical properties of artificial turf systems on soccer players’ performance. A battery of perceptive physiological and physical tests were developed on four different structural systems of artificial turf (System 1: Compacted gravel sub-base without elastic layer; System 2: Compacted gravel sub-base with elastic layer; System 3: Asphalt sub-base without elastic layer; System 4: Asphalt sub-base with elastic layer). The sample was composed of 18 soccer players (22.44±1.72 years) who typically train and compete on artificial turf. The artificial turf system with less rotational traction (S3) showed higher total time in the Repeated Sprint Ability test in comparison to the systems with intermediate values (49.46±1.75 s vs 47.55±1.82 s (S1) and 47.85±1.59 s (S2); p<0.001). The performance in jumping tests (countermovement jump and squat jump) and ball kicking to goal decreased after the RSA test in all surfaces assessed (p<0.05), since the artificial turf system did not affect performance deterioration (p>0.05). The physiological load was similar in all four artificial turf systems. However, players felt more comfortable on the harder and more rigid system (S4; visual analogue scale = 70.83±14.28) than on the softer artificial turf system (S2; visual analogue scale = 54.24±19.63). The lineal regression analysis revealed a significant influence of the mechanical properties of the surface of 16.5%, 15.8% and 7.1% on the mean time of the sprint, the best sprint time and the maximum mean speed in the RSA test respectively. Results suggest a mechanical heterogeneity between the systems of artificial turf which generate differences in the physical performance and in the soccer players’ perceptions. PMID:25354188

The Artificial Hamiltonian, First Integrals, and Closed-Form Solutions of Dynamical Systems for Epidemics

NASA Astrophysics Data System (ADS)

Naz, Rehana; Naeem, Imran

2018-03-01

The non-standard Hamiltonian system, also referred to as a partial Hamiltonian system in the literature, of the form {\\dot q^i} = {partial H}/{partial {p_i}},\\dot p^i = - {partial H}/{partial {q_i}} + {Γ ^i}(t,{q^i},{p_i}) appears widely in economics, physics, mechanics, and other fields. The non-standard (partial) Hamiltonian systems arise from physical Hamiltonian structures as well as from artificial Hamiltonian structures. We introduce the term `artificial Hamiltonian' for the Hamiltonian of a model having no physical structure. We provide here explicitly the notion of an artificial Hamiltonian for dynamical systems of ordinary differential equations (ODEs). Also, we show that every system of second-order ODEs can be expressed as a non-standard (partial) Hamiltonian system of first-order ODEs by introducing an artificial Hamiltonian. This notion of an artificial Hamiltonian gives a new way to solve dynamical systems of first-order ODEs and systems of second-order ODEs that can be expressed as a non-standard (partial) Hamiltonian system by using the known techniques applicable to the non-standard Hamiltonian systems. We employ the proposed notion to solve dynamical systems of first-order ODEs arising in epidemics.

A Philosophical Perspective on Evolutionary Systems Biology

PubMed Central

Soyer, Orkun S.; Siegal, Mark L.

2015-01-01

Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. PMID:26085823

A Review of Safety and Design Requirements of the Artificial Pancreas.

PubMed

Blauw, Helga; Keith-Hynes, Patrick; Koops, Robin; DeVries, J Hans

2016-11-01

As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components-type 1 diabetes, artificial pancreas, and safety or design-and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.

Tracing Primordial Protein Evolution through Structurally Guided Stepwise Segment Elongation*

PubMed Central

Watanabe, Hideki; Yamasaki, Kazuhiko; Honda, Shinya

2014-01-01

The understanding of how primordial proteins emerged has been a fundamental and longstanding issue in biology and biochemistry. For a better understanding of primordial protein evolution, we synthesized an artificial protein on the basis of an evolutionary hypothesis, segment-based elongation starting from an autonomously foldable short peptide. A 10-residue protein, chignolin, the smallest foldable polypeptide ever reported, was used as a structural support to facilitate higher structural organization and gain-of-function in the development of an artificial protein. Repetitive cycles of segment elongation and subsequent phage display selection successfully produced a 25-residue protein, termed AF.2A1, with nanomolar affinity against the Fc region of immunoglobulin G. AF.2A1 shows exquisite molecular recognition ability such that it can distinguish conformational differences of the same molecule. The structure determined by NMR measurements demonstrated that AF.2A1 forms a globular protein-like conformation with the chignolin-derived β-hairpin and a tryptophan-mediated hydrophobic core. Using sequence analysis and a mutation study, we discovered that the structural organization and gain-of-function emerged from the vicinity of the chignolin segment, revealing that the structural support served as the core in both structural and functional development. Here, we propose an evolutionary model for primordial proteins in which a foldable segment serves as the evolving core to facilitate structural and functional evolution. This study provides insights into primordial protein evolution and also presents a novel methodology for designing small sized proteins useful for industrial and pharmaceutical applications. PMID:24356963

Genome sequencing reveals loci under artificial selection that underlie disease phenotypes in the laboratory rat.

PubMed

Atanur, Santosh S; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R; Kaisaki, Pamela J; Otto, Georg W; Ma, Man Chun John; Keane, Thomas M; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J

2013-08-01

Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming

NASA Astrophysics Data System (ADS)

Imani, Moslem; You, Rey-Jer; Kuo, Chung-Yen

2014-10-01

Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE = 0.035) and maximum coefficient of determination (R2 = 0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN.

Genome Sequencing Reveals Loci under Artificial Selection that Underlie Disease Phenotypes in the Laboratory Rat

PubMed Central

Atanur, Santosh S.; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R.; Kaisaki, Pamela J.; Otto, Georg W.; Ma, Man Chun John; Keane, Thomas M.; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R.; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J.; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J.

2013-01-01

Summary Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models. PaperClip PMID:23890820

On the Materials Science of Nature's Arms Race.

PubMed

Liu, Zengqian; Zhang, Zhefeng; Ritchie, Robert O

2018-06-05

Biological material systems have evolved unique combinations of mechanical properties to fulfill their specific function through a series of ingenious designs. Seeking lessons from Nature by replicating the underlying principles of such biological materials offers new promise for creating unique combinations of properties in man-made systems. One case in point is Nature's means of attack and defense. During the long-term evolutionary "arms race," naturally evolved weapons have achieved exceptional mechanical efficiency with a synergy of effective offense and persistence-two characteristics that often tend to be mutually exclusive in many synthetic systems-which may present a notable source of new materials science knowledge and inspiration. This review categorizes Nature's weapons into ten distinct groups, and discusses the unique structural and mechanical designs of each group by taking representative systems as examples. The approach described is to extract the common principles underlying such designs that could be translated into man-made materials. Further, recent advances in replicating the design principles of natural weapons at differing lengthscales in artificial materials, devices and tools to tackle practical problems are revisited, and the challenges associated with biological and bioinspired materials research in terms of both processing and properties are discussed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An artificial nociceptor based on a diffusive memristor.

PubMed

Yoon, Jung Ho; Wang, Zhongrui; Kim, Kyung Min; Wu, Huaqiang; Ravichandran, Vignesh; Xia, Qiangfei; Hwang, Cheol Seong; Yang, J Joshua

2018-01-29

A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the "no adaptation" and "sensitization" phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots.

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

Hybrid Co-Evolutionary Motion Planning via Visibility-Based Repair

NASA Technical Reports Server (NTRS)

Dozier, Gerry; McCullough, Shaun; Brown, Edward, Jr.; Homaifar, Abdollah; Bikdash, Mar-wan

1997-01-01

This paper introduces a hybrid co-evolutionary system for global motion planning within unstructured environments. This system combines the concept of co-evolutionary search along with a concept that we refer to as the visibility-based repair to form a hybrid which quickly transforms infeasible motions into feasible ones. Also, this system makes use of a novel representation scheme for the obstacles within an environment. Our hybrid evolutionary system differs from other evolutionary motion planners in that (1) more emphasis is placed on repairing infeasible motions to develop feasible motions rather than using simulated evolution exclusively as a means of discovering feasible motions, (2) a continuous map of the environment is used rather than a discretized map, and (3) it develops global motion plans for multiple mobile destinations by co-evolving populations of sub-global motion plans. In this paper, we demonstrate the effectiveness of this system by using it to solve two challenging motion planning problems where multiple targets try to move away from a point robot.

Diversification and enrichment of clinical biomaterials inspired by Darwinian evolution.

PubMed

Green, D W; Watson, G S; Watson, J A; Lee, D-J; Lee, J-M; Jung, H-S

2016-09-15

Regenerative medicine and biomaterials design are driven by biomimicry. There is the essential requirement to emulate human cell, tissue, organ and physiological complexity to ensure long-lasting clinical success. Biomimicry projects for biomaterials innovation can be re-invigorated with evolutionary insights and perspectives, since Darwinian evolution is the original dynamic process for biological organisation and complexity. Many existing human inspired regenerative biomaterials (defined as a nature generated, nature derived and nature mimicking structure, produced within a biological system, which can deputise for, or replace human tissues for which it closely matches) are without important elements of biological complexity such as, hierarchy and autonomous actions. It is possible to engineer these essential elements into clinical biomaterials via bioinspired implementation of concepts, processes and mechanisms played out during Darwinian evolution; mechanisms such as, directed, computational, accelerated evolutions and artificial selection contrived in the laboratory. These dynamos for innovation can be used during biomaterials fabrication, but also to choose optimal designs in the regeneration process. Further evolutionary information can help at the design stage; gleaned from the historical evolution of material adaptations compared across phylogenies to changes in their environment and habitats. Taken together, harnessing evolutionary mechanisms and evolutionary pathways, leading to ideal adaptations, will eventually provide a new class of Darwinian and evolutionary biomaterials. This will provide bioengineers with a more diversified and more efficient innovation tool for biomaterial design, synthesis and function than currently achieved with synthetic materials chemistry programmes and rational based materials design approach, which require reasoned logic. It will also inject further creativity, diversity and richness into the biomedical technologies that we make. All of which are based on biological principles. Such evolution-inspired biomaterials have the potential to generate innovative solutions, which match with existing bioengineering problems, in vital areas of clinical materials translation that include tissue engineering, gene delivery, drug delivery, immunity modulation, and scar-less wound healing. Evolution by natural selection is a powerful generator of innovations in molecular, materials and structures. Man has influenced evolution for thousands of years, to create new breeds of farm animals and crop plants, but now molecular and materials can be molded in the same way. Biological molecules and simple structures can be evolved, literally in the laboratory. Furthermore, they are re-designed via lessons learnt from evolutionary history. Through a 3-step process to (1) create variants in material building blocks, (2) screen the variants with beneficial traits/properties and (3) select and support their self-assembly into usable materials, improvements in design and performance can emerge. By introducing biological molecules and small organisms into this process, it is possible to make increasingly diversified, sophisticated and clinically relevant materials for multiple roles in biomedicine. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Impact of seasonality on artificial drainage discharge under temperate climate conditions

Treesearch

Ulrike Hirt; Annett Wetzig; Devandra Amatya; Marisa Matranga

2011-01-01

Artificial drainage systems affect all components of the water and matter balance. For the proper simulation of water and solute fluxes, information is needed about artificial drainage discharge rates and their response times. However, there is relatively little information available about the response of artificial drainage systems to precipitation. To address this...

The Joint Tactical Aerial Resupply Vehicle Impact on Sustainment Operations

DTIC Science & Technology

2017-06-09

Artificial Intelligence , Sustainment Operations, Rifle Company, Autonomous Aerial Resupply, Joint Tactical Autonomous Aerial Resupply System 16...Integrations and Development System AI Artificial Intelligence ARCIC Army Capabilities Integration Center ARDEC Armament Research, Development and...semi- autonomous systems, and fully autonomous systems. Autonomy of machines depends on sophisticated software, including Artificial Intelligence

A novel neural-inspired learning algorithm with application to clinical risk prediction.

PubMed

Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

2015-04-01

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Instructional Applications of Artificial Intelligence.

ERIC Educational Resources Information Center

Halff, Henry M.

1986-01-01

Surveys artificial intelligence and the development of computer-based tutors and speculates on the future of artificial intelligence in education. Includes discussion of the definitions of knowledge, expert systems (computer systems that solve tough technical problems), intelligent tutoring systems (ITS), and specific ITSs such as GUIDON, MYCIN,…

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

The Comet Cometh: Evolving Developmental Systems.

PubMed

Jaeger, Johannes; Laubichler, Manfred; Callebaut, Werner

In a recent opinion piece, Denis Duboule has claimed that the increasing shift towards systems biology is driving evolutionary and developmental biology apart, and that a true reunification of these two disciplines within the framework of evolutionary developmental biology (EvoDevo) may easily take another 100 years. He identifies methodological, epistemological, and social differences as causes for this supposed separation. Our article provides a contrasting view. We argue that Duboule's prediction is based on a one-sided understanding of systems biology as a science that is only interested in functional, not evolutionary, aspects of biological processes. Instead, we propose a research program for an evolutionary systems biology, which is based on local exploration of the configuration space in evolving developmental systems. We call this approach-which is based on reverse engineering, simulation, and mathematical analysis-the natural history of configuration space. We discuss a number of illustrative examples that demonstrate the past success of local exploration, as opposed to global mapping, in different biological contexts. We argue that this pragmatic mode of inquiry can be extended and applied to the mathematical analysis of the developmental repertoire and evolutionary potential of evolving developmental mechanisms and that evolutionary systems biology so conceived provides a pragmatic epistemological framework for the EvoDevo synthesis.

Rapid evolution of hosts begets species diversity at the cost of intraspecific diversity.

PubMed

Frickel, Jens; Theodosiou, Loukas; Becks, Lutz

2017-10-17

Ecosystems are complex food webs in which multiple species interact and ecological and evolutionary processes continuously shape populations and communities. Previous studies on eco-evolutionary dynamics have shown that the presence of intraspecific diversity affects community structure and function, and that eco-evolutionary feedback dynamics can be an important driver for its maintenance. Within communities, feedbacks are, however, often indirect, and they can feed back over many generations. Here, we studied eco-evolutionary feedbacks in evolving communities over many generations and compared two-species systems (virus-host and prey-predator) with a more complex three-species system (virus-host-predator). Both indirect density- and trait-mediated effects drove the dynamics in the complex system, where host-virus coevolution facilitated coexistence of predator and virus, and where coexistence, in return, lowered intraspecific diversity of the host population. Furthermore, ecological and evolutionary dynamics were significantly altered in the three-species system compared with the two-species systems. We found that the predator slowed host-virus coevolution in the complex system and that the virus' effect on the overall population dynamics was negligible when the three species coexisted. Overall, we show that a detailed understanding of the mechanism driving eco-evolutionary feedback dynamics is necessary for explaining trait and species diversity in communities, even in communities with only three species.

Communications and control for electric power systems: Power system stability applications of artificial neural networks

NASA Technical Reports Server (NTRS)

Toomarian, N.; Kirkham, Harold

1994-01-01

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

Genetics of Taste Receptors

PubMed Central

Bachmanov, Alexander A.; Bosak, Natalia P.; Lin, Cailu; Matsumoto, Ichiro; Ohmoto, Makoto; Reed, Danielle R.; Nelson, Theodore M.

2016-01-01

Taste receptors function as one of the interfaces between internal and external milieus. Taste receptors for sweet and umami (T1R [taste receptor, type 1]), bitter (T2R [taste receptor, type 2]), and salty (ENaC [epithelial sodium channel]) have been discovered in the recent years, but transduction mechanisms of sour taste and ENaC-independent salt taste are still poorly understood. In addition to these five main taste qualities, the taste system detects such noncanonical “tastes” as water, fat, and complex carbohydrates, but their reception mechanisms require further research. Variations in taste receptor genes between and within vertebrate species contribute to individual and species differences in taste-related behaviors. These variations are shaped by evolutionary forces and reflect species adaptations to their chemical environments and feeding ecology. Principles of drug discovery can be applied to taste receptors as targets in order to develop novel taste compounds to satisfy demand in better artificial sweeteners, enhancers of sugar and sodium taste, and blockers of bitterness of food ingredients and oral medications. PMID:23886383

Modelling formulations using gene expression programming--a comparative analysis with artificial neural networks.

PubMed

Colbourn, E A; Roskilly, S J; Rowe, R C; York, P

2011-10-09

This study has investigated the utility and potential advantages of gene expression programming (GEP)--a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system--to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R(2) these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data. Copyright © 2011 Elsevier B.V. All rights reserved.

Simulated Breeding

NASA Astrophysics Data System (ADS)

Unemi, Tatsuo

This chapter describes a basic framework of simulated breeding, a type of interactive evolutionary computing to breed artifacts, whose origin is Blind Watchmaker by Dawkins. These methods make it easy for humans to design a complex object adapted to his/her subjective criteria, just similarly to agricultural products we have been developing over thousands of years. Starting from randomly initialized genome, the solution candidates are improved through several generations with artificial selection. The graphical user interface helps the process of breeding with techniques of multifield user interface and partial breeding. The former improves the diversity of individuals that prevents being trapped at local optimum. The latter makes it possible for the user to fix features he/she already satisfied. These methods were examined through artistic applications by the author: SBART for graphics art and SBEAT for music. Combining with a direct genome editor and exportation to another graphical or musical tool on the computer, they can be powerful tools for artistic creation. These systems may contribute to the creation of a type of new culture.

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Decision-making and problem-solving methods in automation technology

NASA Technical Reports Server (NTRS)

Hankins, W. W.; Pennington, J. E.; Barker, L. K.

1983-01-01

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data.

PubMed

Linard, Benjamin; Nguyen, Ngoc Hoan; Prosdocimi, Francisco; Poch, Olivier; Thompson, Julie D

2012-01-01

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.

Some assembly required: evolutionary and systems perspectives on the mammalian reproductive system.

PubMed

Mordhorst, Bethany R; Wilson, Miranda L; Conant, Gavin C

2016-01-01

In this review, we discuss the way that insights from evolutionary theory and systems biology shed light on form and function in mammalian reproductive systems. In the first part of the review, we contrast the rapid evolution seen in some reproductive genes with the generally conservative nature of development. We discuss directional selection and coevolution as potential drivers of rapid evolution in sperm and egg proteins. Such rapid change is very different from the highly conservative nature of later embryo development. However, it is not unique, as some regions of the sex chromosomes also show elevated rates of evolutionary change. To explain these contradictory trends, we argue that it is not reproductive functions per se that induce rapid evolution. Rather, it is the fact that biotic interactions, such as speciation events and sexual conflict, have no evolutionary endpoint and hence can drive continuous evolutionary changes. Returning to the question of sex chromosome evolution, we discuss the way that recent advances in evolutionary genomics and systems biology and, in particular, the development of a theory of gene balance provide a better understanding of the evolutionary patterns seen on these chromosomes. We end the review with a discussion of a surprising and incompletely understood phenomenon observed in early embryos: namely the Warburg effect, whereby glucose is fermented to lactate and alanine rather than respired to carbon dioxide. We argue that evolutionary insights, from both yeasts and tumor cells, help to explain the Warburg effect, and that new metabolic modeling approaches are useful in assessing the potential sources of the effect.

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.

Many-objective optimization and visual analytics reveal key trade-offs for London's water supply

NASA Astrophysics Data System (ADS)

Matrosov, Evgenii S.; Huskova, Ivana; Kasprzyk, Joseph R.; Harou, Julien J.; Lambert, Chris; Reed, Patrick M.

2015-12-01

In this study, we link a water resource management simulator to multi-objective search to reveal the key trade-offs inherent in planning a real-world water resource system. We consider new supplies and demand management (conservation) options while seeking to elucidate the trade-offs between the best portfolios of schemes to satisfy projected water demands. Alternative system designs are evaluated using performance measures that minimize capital and operating costs and energy use while maximizing resilience, engineering and environmental metrics, subject to supply reliability constraints. Our analysis shows many-objective evolutionary optimization coupled with state-of-the art visual analytics can help planners discover more diverse water supply system designs and better understand their inherent trade-offs. The approach is used to explore future water supply options for the Thames water resource system (including London's water supply). New supply options include a new reservoir, water transfers, artificial recharge, wastewater reuse and brackish groundwater desalination. Demand management options include leakage reduction, compulsory metering and seasonal tariffs. The Thames system's Pareto approximate portfolios cluster into distinct groups of water supply options; for example implementing a pipe refurbishment program leads to higher capital costs but greater reliability. This study highlights that traditional least-cost reliability constrained design of water supply systems masks asset combinations whose benefits only become apparent when more planning objectives are considered.

Eco-evolutionary feedbacks drive species interactions

PubMed Central

Andrade-Domínguez, Andrés; Salazar, Emmanuel; del Carmen Vargas-Lagunas, María; Kolter, Roberto; Encarnación, Sergio

2014-01-01

In the biosphere, many species live in close proximity and can thus interact in many different ways. Such interactions are dynamic and fall along a continuum between antagonism and cooperation. Because interspecies interactions are the key to understanding biological communities, it is important to know how species interactions arise and evolve. Here, we show that the feedback between ecological and evolutionary processes has a fundamental role in the emergence and dynamics of species interaction. Using a two-species artificial community, we demonstrate that ecological processes and rapid evolution interact to influence the dynamics of the symbiosis between a eukaryote (Saccharomyces cerevisiae) and a bacterium (Rhizobium etli). The simplicity of our experimental design enables an explicit statement of causality. The niche-constructing activities of the fungus were the key ecological process: it allowed the establishment of a commensal relationship that switched to ammensalism and provided the selective conditions necessary for the adaptive evolution of the bacteria. In this latter state, the bacterial population radiates into more than five genotypes that vary with respect to nutrient transport, metabolic strategies and global regulation. Evolutionary diversification of the bacterial populations has strong effects on the community; the nature of interaction subsequently switches from ammensalism to antagonism where bacteria promote yeast extinction. Our results demonstrate the importance of the evolution-to-ecology pathway in the persistence of interactions and the stability of communities. Thus, eco-evolutionary dynamics have the potential to transform the structure and functioning of ecosystems. Our results suggest that these dynamics should be considered to improve our understanding of beneficial and detrimental host–microbe interactions. PMID:24304674

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

PubMed Central

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Approaching neuropsychological tasks through adaptive neurorobots

NASA Astrophysics Data System (ADS)

Gigliotta, Onofrio; Bartolomeo, Paolo; Miglino, Orazio

2015-04-01

Neuropsychological phenomena have been modelized mainly, by the mainstream approach, by attempting to reproduce their neural substrate whereas sensory-motor contingencies have attracted less attention. In this work, we introduce a simulator based on the evolutionary robotics platform Evorobot* in order to setting up in silico neuropsychological tasks. Moreover, in this study we trained artificial embodied neurorobotic agents equipped with a pan/tilt camera, provided with different neural and motor capabilities, to solve a well-known neuropsychological test: the cancellation task in which an individual is asked to cancel target stimuli surrounded by distractors. Results showed that embodied agents provided with additional motor capabilities (a zooming/attentional actuator) outperformed simple pan/tilt agents, even those equipped with more complex neural controllers and that the zooming ability is exploited to correctly categorising presented stimuli. We conclude that since the sole neural computational power cannot explain the (artificial) cognition which emerged throughout the adaptive process, such kind of modelling approach can be fruitful in neuropsychological modelling where the importance of having a body is often neglected.

Hierarchical artificial bee colony algorithm for RFID network planning optimization.

PubMed

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase.

PubMed

Obexer, Richard; Godina, Alexei; Garrabou, Xavier; Mittl, Peer R E; Baker, David; Griffiths, Andrew D; Hilvert, Donald

2017-01-01

Designing catalysts that achieve the rates and selectivities of natural enzymes is a long-standing goal in protein chemistry. Here, we show that an ultrahigh-throughput droplet-based microfluidic screening platform can be used to improve a previously optimized artificial aldolase by an additional factor of 30 to give a >10 9 rate enhancement that rivals the efficiency of class I aldolases. The resulting enzyme catalyses a reversible aldol reaction with high stereoselectivity and tolerates a broad range of substrates. Biochemical and structural studies show that catalysis depends on a Lys-Tyr-Asn-Tyr tetrad that emerged adjacent to a computationally designed hydrophobic pocket during directed evolution. This constellation of residues is poised to activate the substrate by Schiff base formation, promote mechanistically important proton transfers and stabilize multiple transition states along a complex reaction coordinate. The emergence of such a sophisticated catalytic centre shows that there is nothing magical about the catalytic activities or mechanisms of naturally occurring enzymes, or the evolutionary process that gave rise to them.

A framework for the comparative study of language.

PubMed

Uriagereka, Juan; Reggia, James A; Wilkinson, Gerald S

2013-07-18

Comparative studies of language are difficult because few language precursors are recognized. In this paper we propose a framework for designing experiments that test for structural and semantic patterns indicative of simple or complex grammars as originally described by Chomsky. We argue that a key issue is whether animals can recognize full recursion, which is the hallmark of context-free grammar. We discuss limitations of recent experiments that have attempted to address this issue, and point out that experiments aimed at detecting patterns that follow a Fibonacci series have advantages over other artificial context-free grammars. We also argue that experiments using complex sequences of behaviors could, in principle, provide evidence for fully recursive thought. Some of these ideas could also be approached using artificial life simulations, which have the potential to reveal the types of evolutionary transitions that could occur over time. Because the framework we propose has specific memory and computational requirements, future experiments could target candidate genes with the goal of revealing the genetic underpinnings of complex cognition.

Error baseline rates of five sample preparation methods used to characterize RNA virus populations.

PubMed

Kugelman, Jeffrey R; Wiley, Michael R; Nagle, Elyse R; Reyes, Daniel; Pfeffer, Brad P; Kuhn, Jens H; Sanchez-Lockhart, Mariano; Palacios, Gustavo F

2017-01-01

Individual RNA viruses typically occur as populations of genomes that differ slightly from each other due to mutations introduced by the error-prone viral polymerase. Understanding the variability of RNA virus genome populations is critical for understanding virus evolution because individual mutant genomes may gain evolutionary selective advantages and give rise to dominant subpopulations, possibly even leading to the emergence of viruses resistant to medical countermeasures. Reverse transcription of virus genome populations followed by next-generation sequencing is the only available method to characterize variation for RNA viruses. However, both steps may lead to the introduction of artificial mutations, thereby skewing the data. To better understand how such errors are introduced during sample preparation, we determined and compared error baseline rates of five different sample preparation methods by analyzing in vitro transcribed Ebola virus RNA from an artificial plasmid-based system. These methods included: shotgun sequencing from plasmid DNA or in vitro transcribed RNA as a basic "no amplification" method, amplicon sequencing from the plasmid DNA or in vitro transcribed RNA as a "targeted" amplification method, sequence-independent single-primer amplification (SISPA) as a "random" amplification method, rolling circle reverse transcription sequencing (CirSeq) as an advanced "no amplification" method, and Illumina TruSeq RNA Access as a "targeted" enrichment method. The measured error frequencies indicate that RNA Access offers the best tradeoff between sensitivity and sample preparation error (1.4-5) of all compared methods.

Error baseline rates of five sample preparation methods used to characterize RNA virus populations

PubMed Central

Kugelman, Jeffrey R.; Wiley, Michael R.; Nagle, Elyse R.; Reyes, Daniel; Pfeffer, Brad P.; Kuhn, Jens H.; Sanchez-Lockhart, Mariano; Palacios, Gustavo F.

2017-01-01

Individual RNA viruses typically occur as populations of genomes that differ slightly from each other due to mutations introduced by the error-prone viral polymerase. Understanding the variability of RNA virus genome populations is critical for understanding virus evolution because individual mutant genomes may gain evolutionary selective advantages and give rise to dominant subpopulations, possibly even leading to the emergence of viruses resistant to medical countermeasures. Reverse transcription of virus genome populations followed by next-generation sequencing is the only available method to characterize variation for RNA viruses. However, both steps may lead to the introduction of artificial mutations, thereby skewing the data. To better understand how such errors are introduced during sample preparation, we determined and compared error baseline rates of five different sample preparation methods by analyzing in vitro transcribed Ebola virus RNA from an artificial plasmid-based system. These methods included: shotgun sequencing from plasmid DNA or in vitro transcribed RNA as a basic “no amplification” method, amplicon sequencing from the plasmid DNA or in vitro transcribed RNA as a “targeted” amplification method, sequence-independent single-primer amplification (SISPA) as a “random” amplification method, rolling circle reverse transcription sequencing (CirSeq) as an advanced “no amplification” method, and Illumina TruSeq RNA Access as a “targeted” enrichment method. The measured error frequencies indicate that RNA Access offers the best tradeoff between sensitivity and sample preparation error (1.4−5) of all compared methods. PMID:28182717

Structure, tissue distribution, and chromosomal localization of the prepronociceptin gene.

PubMed

Mollereau, C; Simons, M J; Soularue, P; Liners, F; Vassart, G; Meunier, J C; Parmentier, M

1996-08-06

Nociceptin (orphanin FQ), the newly discovered natural agonist of opioid receptor-like (ORL1) receptor, is a neuropeptide that is endowed with pronociceptive activity in vivo. Nociceptin is derived from a larger precursor, prepronociceptin (PPNOC), whose human, mouse, and rat genes we have now isolated. The PPNOC gene is highly conserved in the three species and displays organizational features that are strikingly similar to those of the genes of preproenkephalin, preprodynorphin, and preproopiomelanocortin, the precursors to endogenous opioid peptides, suggesting the four genes belong to the same family-i.e., have a common evolutionary origin. The PPNOC gene encodes a single copy of nociceptin as well as of other peptides whose sequence is strictly conserved across murine and human species; hence it is likely to be neurophysiologically significant. Northern blot analysis shows that the PPNOC gene is predominantly transcribed in the central nervous system (brain and spinal cord) and, albeit weakly, in the ovary, the sole peripheral organ expressing the gene. By using a radiation hybrid cell line panel, the PPNOC gene was mapped to the short arm of human chromosome 8 (8p21), between sequence-tagged site markers WI-5833 and WI-1172, in close proximity of the locus encoding the neurofilament light chain NEFL. Analysis of yeast artificial chromosome clones belonging to the WC8.4 contig covering the 8p21 region did not allow to detect the presence of the gene on these yeast artificial chromosomes, suggesting a gap in the coverage within this contig.

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

DOT National Transportation Integrated Search

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

Investigation of rat exploratory behavior via evolving artificial neural networks.

PubMed

Costa, Ariadne de Andrade; Tinós, Renato

2016-09-01

Neuroevolution comprises the use of evolutionary computation to define the architecture and/or to train artificial neural networks (ANNs). This strategy has been employed to investigate the behavior of rats in the elevated plus-maze, which is a widely used tool for studying anxiety in mice and rats. Here we propose a neuroevolutionary model, in which both the weights and the architecture of artificial neural networks (our virtual rats) are evolved by a genetic algorithm. This model is an improvement of a previous model that involves the evolution of just the weights of the ANN by the genetic algorithm. In order to compare both models, we analyzed traditional measures of anxiety behavior, like the time spent and the number of entries in both open and closed arms of the maze. When compared to real rat data, our findings suggest that the results from the model introduced here are statistically better than those from other models in the literature. In this way, the neuroevolution of architecture is clearly important for the development of the virtual rats. Moreover, this technique allowed the comprehension of the importance of different sensory units and different number of hidden neurons (performing as memory) in the ANNs (virtual rats). Copyright © 2016 Elsevier B.V. All rights reserved.

Artificial Neural Network Analysis System

DTIC Science & Technology

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

DTIC Science & Technology

1990-06-01

NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

A Primer for Problem Solving Using Artificial Intelligence.

ERIC Educational Resources Information Center

Schell, George P.

1988-01-01

Reviews the development of artificial intelligence systems and the mechanisms used, including knowledge representation, programing languages, and problem processing systems. Eleven books and 6 journals are listed as sources of information on artificial intelligence. (23 references) (CLB)

Through Sex, Nature Is Telling Us Something Important.

PubMed

Kondrashov, Alexey S

2018-05-01

Theoretically, a variety of mechanisms can make amphimixis advantageous due to reshuffling of offspring genotypes. Recently, it has been shown experimentally that some of these mechanisms can indeed work in artificial populations. However, we still do not know which of them, if any, are relevant in nature, and the available indirect estimates seem to suggest that neither negative nor positive selection in natural populations is strong enough to provide evolutionary protection for obligate amphimixis. Thus, progress in understanding the evolution of amphimixis will depend on direct measurements of the strength of natural selection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

The deconvolution of complex spectra by artificial immune system

NASA Astrophysics Data System (ADS)

Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

2017-11-01

An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

Rapid evolution of hosts begets species diversity at the cost of intraspecific diversity

PubMed Central

Frickel, Jens; Theodosiou, Loukas

2017-01-01

Ecosystems are complex food webs in which multiple species interact and ecological and evolutionary processes continuously shape populations and communities. Previous studies on eco-evolutionary dynamics have shown that the presence of intraspecific diversity affects community structure and function, and that eco-evolutionary feedback dynamics can be an important driver for its maintenance. Within communities, feedbacks are, however, often indirect, and they can feed back over many generations. Here, we studied eco-evolutionary feedbacks in evolving communities over many generations and compared two-species systems (virus–host and prey–predator) with a more complex three-species system (virus–host–predator). Both indirect density- and trait-mediated effects drove the dynamics in the complex system, where host–virus coevolution facilitated coexistence of predator and virus, and where coexistence, in return, lowered intraspecific diversity of the host population. Furthermore, ecological and evolutionary dynamics were significantly altered in the three-species system compared with the two-species systems. We found that the predator slowed host–virus coevolution in the complex system and that the virus’ effect on the overall population dynamics was negligible when the three species coexisted. Overall, we show that a detailed understanding of the mechanism driving eco-evolutionary feedback dynamics is necessary for explaining trait and species diversity in communities, even in communities with only three species. PMID:28973943

Artificial Intelligence Measurement System, Overview and Lessons Learned. Final Project Report.

ERIC Educational Resources Information Center

Baker, Eva L.; Butler, Frances A.

This report summarizes the work conducted for the Artificial Intelligence Measurement System (AIMS) Project which was undertaken as an exploration of methodology to consider how the effects of artificial intelligence systems could be compared to human performance. The research covered four areas of inquiry: (1) natural language processing and…

Cooperative combinatorial optimization: evolutionary computation case study.

PubMed

Burgin, Mark; Eberbach, Eugene

2008-01-01

This paper presents a formalization of the notion of cooperation and competition of multiple systems that work toward a common optimization goal of the population using evolutionary computation techniques. It is proved that evolutionary algorithms are more expressive than conventional recursive algorithms, such as Turing machines. Three classes of evolutionary computations are introduced and studied: bounded finite, unbounded finite, and infinite computations. Universal evolutionary algorithms are constructed. Such properties of evolutionary algorithms as completeness, optimality, and search decidability are examined. A natural extension of evolutionary Turing machine (ETM) model is proposed to properly reflect phenomena of cooperation and competition in the whole population.

A molecular signaling approach to linking intraspecific variation and macro-evolutionary patterns.

PubMed

Swanson, Eli M; Snell-Rood, Emilie C

2014-11-01

Macro-evolutionary comparisons are a valued tool in evolutionary biology. Nevertheless, our understanding of how systems involved in molecular signaling change in concert with phenotypic diversification has lagged. We argue that integrating our understanding of the evolution of molecular signaling systems with phylogenetic comparative methods is an important step toward understanding the processes linking variation among individuals with variation among species. Focusing mostly on the endocrine system, we discuss how the complexity and mechanistic nature of molecular signaling systems may influence the application and interpretation of macro-evolutionary comparisons. We also detail five hypotheses concerning the role that physiological mechanisms can play in shaping macro-evolutionary patterns, and discuss ways in which these hypotheses could influence phenotypic diversification. Finally, we review a series of tools able to analyze the complexity of physiological systems and the way they change in concert with the phenotypes for which they coordinate development. © The Author 2014. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Stability of Bifurcating Stationary Solutions of the Artificial Compressible System

NASA Astrophysics Data System (ADS)

Teramoto, Yuka

2018-02-01

The artificial compressible system gives a compressible approximation of the incompressible Navier-Stokes system. The latter system is obtained from the former one in the zero limit of the artificial Mach number ɛ which is a singular limit. The sets of stationary solutions of both systems coincide with each other. It is known that if a stationary solution of the incompressible system is asymptotically stable and the velocity field of the stationary solution satisfies an energy-type stability criterion, then it is also stable as a solution of the artificial compressible one for sufficiently small ɛ . In general, the range of ɛ shrinks when the spectrum of the linearized operator for the incompressible system approaches to the imaginary axis. This can happen when a stationary bifurcation occurs. It is proved that when a stationary bifurcation from a simple eigenvalue occurs, the range of ɛ can be taken uniformly near the bifurcation point to conclude the stability of the bifurcating solution as a solution of the artificial compressible system.

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

PubMed Central

Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

2017-01-01

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

PubMed

De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

2017-10-31

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

Dynamical Systems and Motion Vision.

DTIC Science & Technology

1988-04-01

TASK Artificial Inteligence Laboratory AREA I WORK UNIT NUMBERS 545 Technology Square . Cambridge, MA 02139 C\\ II. CONTROLLING OFFICE NAME ANO0 ADDRESS...INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY A.I.Memo No. 1037 April, 1988 Dynamical Systems and Motion Vision Joachim Heel Abstract: In this... Artificial Intelligence L3 Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory’s [1 Artificial Intelligence Research is

Discrete particle swarm optimization for identifying community structures in signed social networks.

PubMed

Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

2014-10-01

Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evolution of Signaling in a Multi-Robot System: Categorization and Communication

NASA Astrophysics Data System (ADS)

Ampatzis, Christos; Tuci, Elio; Trianni, Vito; Dorigo, Marco

We use Evolutionary Robotics to design robot controllers in which decision-making mechanisms to switch from solitary to social behavior are integrated with the mechanisms that underpin the sensory-motor repertoire of the robots. In particular, we study the evolution of behavioral and communicative skills in a categorization task. The individual decision-making structures are based on the integration over time of sensory information. The mechanisms for switching from solitary to social behavior and the ways in which the robots can affect each other's behavior are not predetermined by the experimenter, but are aspects of our model designed by artificial evolution. Our results show that evolved robots manage to cooperate and collectively discriminate between different environments by developing a simple communication protocol based on sound signaling. Communication emerges in the absence of explicit selective pressure coded in the fitness function. The evolution of communication is neither trivial nor obvious; for a meaningful signaling system to evolve, evolution must produce both appropriate signals and appropriate reactions to signals. The use of communication proves to be adaptive for the group, even if, in principle, non-cooperating robots can be equally successful with cooperating robots.

Current topics in glycemic control by wearable artificial pancreas or bedside artificial pancreas with closed-loop system.

PubMed

Hanazaki, Kazuhiro; Munekage, Masaya; Kitagawa, Hiroyuki; Yatabe, Tomoaki; Munekage, Eri; Shiga, Mai; Maeda, Hiromichi; Namikawa, Tsutomu

2016-09-01

The incidence of diabetes is increasing at an unprecedented pace and has become a serious health concern worldwide during the last two decades. Despite this, adequate glycemic control using an artificial pancreas has not been established, although the 21st century has seen rapid developments in this area. Herein, we review current topics in glycemic control for both the wearable artificial pancreas for type 1 and type 2 diabetic patients and the bedside artificial pancreas for surgical diabetic patients. In type 1 diabetic patients, nocturnal hypoglycemia associated with insulin therapy remains a serious problem that could be addressed by the recent development of a wearable artificial pancreas. This smart phone-like device, comprising a real-time, continuous glucose monitoring system and insulin pump system, could potentially significantly reduce nocturnal hypoglycemia compared with conventional glycemic control. Of particular interest in this space are the recent inventions of a low-glucose suspend feature in the portable systems that automatically stops insulin delivery 2 h following a glucose sensor value <70 mg/dL and a bio-hormonal pump system consisting of insulin and glucagon pumps. Perioperative tight glycemic control using a bedside artificial pancreas with the closed-loop system has also proved safe and effective for not only avoiding hypoglycemia, but also for reducing blood glucose level variability resulting in good surgical outcomes. We hope that a more sophisticated artificial pancreas with closed-loop system will now be taken up for routine use worldwide, providing enormous relief for patients suffering from uncontrolled hyperglycemia, hypoglycemia, and/or variability in blood glucose concentrations.

Differences in the selection response of serially repeated color pattern characters: standing variation, development, and evolution.

PubMed

Allen, Cerisse E; Beldade, Patrícia; Zwaan, Bas J; Brakefield, Paul M

2008-03-26

There is spectacular morphological diversity in nature but lineages typically display a limited range of phenotypes. Because developmental processes generate the phenotypic variation that fuels natural selection, they are a likely source of evolutionary biases, facilitating some changes and limiting others. Although shifts in developmental regulation are associated with morphological differences between taxa, it is unclear how underlying mechanisms affect the rate and direction of evolutionary change within populations under selection. Here we focus on two ecologically relevant features of butterfly wing color patterns, eyespot size and color composition, which are similarly and strongly correlated across the serially repeated eyespots. Though these two characters show similar patterns of standing variation and covariation within a population, they differ in key features of their underlying development. We targeted pairs of eyespots with artificial selection for coordinated (concerted selection) versus independent (antagonistic selection) change in their color composition and size and compared evolutionary responses of the two color pattern characters. The two characters respond to selection in strikingly different ways despite initially similar patterns of variation in all directions present in the starting population. Size (determined by local properties of a diffusing inductive signal) evolves flexibly in all selected directions. However, color composition (determined by a tissue-level response to the signal concentration gradient) evolves only in the direction of coordinated change. There was no independent evolutionary change in the color composition of two eyespots in response to antagonistic selection. Moreover, these differences in the directions of short-term evolutionary change in eyespot size and color composition within a single species are consistent with the observed wing pattern diversity in the genus. Both characters respond rapidly to selection for coordinated change, but there are striking differences in their response to selection for antagonistic, independent change across eyespots. While many additional factors may contribute to both short- and long-term evolutionary response, we argue that the compartmentalization of developmental processes can influence the diversification of serial repeats such as butterfly eyespots, even under strong selection.

An overview of ecological and evolutionary research on disease in natural systems: an annotated reference list

Treesearch

Helen M. Alexander

2012-01-01

The Fourth International Workshop on the Genetics of Host-Parasite Interactions in Forestry (July 31-August 5, 2011) included a session on “Ecology and Evolutionary Biology of Resistance and Tolerance, Natural Systems.” Within this session, I gave a talk entitled “An overview of ecological and evolutionary research on disease in ‘natural’ systems” that reviewed...

In Pursuit of Artificial Intelligence.

ERIC Educational Resources Information Center

Watstein, Sarah; Kesselman, Martin

1986-01-01

Defines artificial intelligence and reviews current research in natural language processing, expert systems, and robotics and sensory systems. Discussion covers current commercial applications of artificial intelligence and projections of uses and limitations in library technical and public services, e.g., in cataloging and online information and…

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Moglichkeiten und Grenzen eines evolutionaren Paradigmas in der Erziehungswissenschaft (Possibilities and Limits of an Evolutionary Paradigm in Educational Science).

ERIC Educational Resources Information Center

Nipkow, Karl Ernst

2002-01-01

Describes a sectoral and paradigmatic approach to evolutionary research. Argues that an evolutionary paradigm does not exist. Examines the socio-biological approach and that of a system-theoretical oriented general evolutionary theory. Utilizes the topics of cooperation, delimitation, and indoctrination to explain more promising ways of adoption.…

A Phylogenetic, Biogeographic, and Taxonomic study of all Extant Species of Anolis (Squamata; Iguanidae).

PubMed

Poe, Steven; Nieto-Montes de Oca, Adrián; Torres-Carvajal, Omar; De Queiroz, Kevin; Velasco, Julián A; Truett, Brad; Gray, Levi N; Ryan, Mason J; Köhler, Gunther; Ayala-Varela, Fernando; Latella, Ian

2017-09-01

Anolis lizards (anoles) are textbook study organisms in evolution and ecology. Although several topics in evolutionary biology have been elucidated by the study of anoles, progress in some areas has been hampered by limited phylogenetic information on this group. Here, we present a phylogenetic analysis of all 379 extant species of Anolis, with new phylogenetic data for 139 species including new DNA data for 101 species. We use the resulting estimates as a basis for defining anole clade names under the principles of phylogenetic nomenclature and to examine the biogeographic history of anoles. Our new taxonomic treatment achieves the supposed advantages of recent subdivisions of anoles that employed ranked Linnaean-based nomenclature while avoiding the pitfalls of those approaches regarding artificial constraints imposed by ranks. Our biogeographic analyses demonstrate complexity in the dispersal history of anoles, including multiple crossings of the Isthmus of Panama, two invasions of the Caribbean, single invasions to Jamaica and Cuba, and a single evolutionary dispersal from the Caribbean to the mainland that resulted in substantial anole diversity. Our comprehensive phylogenetic estimate of anoles should prove useful for rigorous testing of many comparative evolutionary hypotheses. [Anoles; biogeography; lizards; Neotropics; phylogeny; taxonomy]. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Accelerated rates of protein evolution in barley grain and pistil biased genes might be legacy of domestication.

PubMed

Shi, Tao; Dimitrov, Ivan; Zhang, Yinling; Tax, Frans E; Yi, Jing; Gou, Xiaoping; Li, Jia

2015-10-01

Traits related to grain and reproductive organs in grass crops have been under continuous directional selection during domestication. Barley is one of the oldest domesticated crops in human history. Thus genes associated with the grain and reproductive organs in barley may show evidence of dramatic evolutionary change. To understand how artificial selection contributes to protein evolution of biased genes in different barley organs, we used Digital Gene Expression analysis of six barley organs (grain, pistil, anther, leaf, stem and root) to identify genes with biased expression in specific organs. Pairwise comparisons of orthologs between barley and Brachypodium distachyon, as well as between highland and lowland barley cultivars mutually indicated that grain and pistil biased genes show relatively higher protein evolutionary rates compared with the median of all orthologs and other organ biased genes. Lineage-specific protein evolutionary rates estimation showed similar patterns with elevated protein evolution in barley grain and pistil biased genes, yet protein sequences generally evolve much faster in the lowland barley cultivar. Further functional annotations revealed that some of these grain and pistil biased genes with rapid protein evolution are related to nutrient biosynthesis and cell cycle/division. Our analyses provide insights into how domestication differentially shaped the evolution of genes specific to different organs of a crop species, and implications for future functional studies of domestication genes.

Experimental results in evolutionary fault-recovery for field programmable analog devices

NASA Technical Reports Server (NTRS)

Zebulum, Ricardo S.; Keymeulen, Didier; Duong, Vu; Guo, Xin; Ferguson, M. I.; Stoica, Adrian

2003-01-01

This paper presents experimental results of fast intrinsic evolutionary design and evolutionary fault recovery of a 4-bit Digital to Analog Converter (DAC) using the JPL stand-alone board-level evolvable system (SABLES).

Applications of artificial intelligence V; Proceedings of the Meeting, Orlando, FL, May 18-20, 1987

NASA Technical Reports Server (NTRS)

Gilmore, John F. (Editor)

1987-01-01

The papers contained in this volume focus on current trends in applications of artificial intelligence. Topics discussed include expert systems, image understanding, artificial intelligence tools, knowledge-based systems, heuristic systems, manufacturing applications, and image analysis. Papers are presented on expert system issues in automated, autonomous space vehicle rendezvous; traditional versus rule-based programming techniques; applications to the control of optional flight information; methodology for evaluating knowledge-based systems; and real-time advisory system for airborne early warning.

Artificial Intelligence and Spacecraft Power Systems

NASA Technical Reports Server (NTRS)

Dugel-Whitehead, Norma R.

1997-01-01

This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.

A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems

DTIC Science & Technology

1990-11-01

Intelligence Systems," in Distributed Artifcial Intelligence , vol. II, L. Gasser and M. Huhns (eds), Pitman, London, 1989, pp. 413-430. Shaw, M. Harrow, B...IDTIC FILE COPY A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems N Michael I. Shaw...SUBTITLE 5. FUNDING NUMBERS A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems 6

Common evolutionary trends underlie the four-bar linkage systems of sunfish and mantis shrimp.

PubMed

Hu, Yinan; Nelson-Maney, Nathan; Anderson, Philip S L

2017-05-01

Comparative biomechanics offers an opportunity to explore the evolution of disparate biological systems that share common underlying mechanics. Four-bar linkage modeling has been applied to various biological systems such as fish jaws and crustacean appendages to explore the relationship between biomechanics and evolutionary diversification. Mechanical sensitivity states that the functional output of a mechanical system will show differential sensitivity to changes in specific morphological components. We document similar patterns of mechanical sensitivity in two disparate four-bar systems from different phyla: the opercular four-bar system in centrarchid fishes and the raptorial appendage of stomatopods. We built dynamic linkage models of 19 centrarchid and 36 stomatopod species and used phylogenetic generalized least squares regression (PGLS) to compare evolutionary shifts in linkage morphology and mechanical outputs derived from the models. In both systems, the kinematics of the four-bar mechanism show significant evolutionary correlation with the output link, while travel distance of the output arm is correlated with the coupler link. This common evolutionary pattern seen in both fish and crustacean taxa is a potential consequence of the mechanical principles underlying four-bar systems. Our results illustrate the potential influence of physical principles on morphological evolution across biological systems with different structures, behaviors, and ecologies. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Evolutionary orbital period change in BH Virginis

NASA Astrophysics Data System (ADS)

Gebrehiwot, Y. M.; Tessema, S. B.; Berdnikov, L. N.

2017-04-01

The study of orbital period change of close binaries, such as BH Virginis (BH Vir), using very long time baseline is vital to understand evolutionary processes of the system. In this paper, we use photometric data to analyze the evolutionary orbital period change of the short period RS CVn-type binary system, BH Vir, with a time baseline spanning 123 years. We used the software version of the Hertzsprung method to describe the O-C curve of the system, and we found that the orbital period secularly decreases at a rate of dp/dt=-(0.0013000 ± 0.0000863) s yr^{-1}. Because BH Vir is a typical detached binary system and both components are late type (G0 V + G2 V) stars, the evolutionary period change could be caused by the angular momentum loss due to tides coupled with magnetic breaking.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

An evolutionary framework for cultural change: Selectionism versus communal exchange

NASA Astrophysics Data System (ADS)

Gabora, Liane

2013-06-01

Dawkins' replicator-based conception of evolution has led to widespread mis-application of selectionism across the social sciences because it does not address the paradox that necessitated the theory of natural selection in the first place: how do organisms accumulate change when traits acquired over their lifetime are obliterated? This is addressed by von Neumann's concept of a self-replicating automaton (SRA). A SRA consists of a self-assembly code that is used in two distinct ways: (1) actively deciphered during development to construct a self-similar replicant, and (2) passively copied to the replicant to ensure that it can reproduce. Information that is acquired over a lifetime is not transmitted to offspring, whereas information that is inherited during copying is transmitted. In cultural evolution there is no mechanism for discarding acquired change. Acquired change can accumulate orders of magnitude faster than, and quickly overwhelm, inherited change due to differential replication of variants in response to selection. This prohibits a selectionist but not an evolutionary framework for culture and the creative processes that fuel it. The importance non-Darwinian processes in biological evolution is increasingly recognized. Recent work on the origin of life suggests that early life evolved through a non-Darwinian process referred to as communal exchange that does not involve a self-assembly code, and that natural selection emerged from this more haphazard, ancestral evolutionary process. It is proposed that communal exchange provides an evolutionary framework for culture that enables specification of cognitive features necessary for a (real or artificial) societies to evolve culture. This is supported by a computational model of cultural evolution and a conceptual network based program for documenting material cultural history, and it is consistent with high levels of human cooperation.

Stochastic dynamics and stable equilibrium of evolutionary optional public goods game in finite populations

NASA Astrophysics Data System (ADS)

Quan, Ji; Liu, Wei; Chu, Yuqing; Wang, Xianjia

2018-07-01

Continuous noise caused by mutation is widely present in evolutionary systems. Considering the noise effects and under the optional participation mechanism, a stochastic model for evolutionary public goods game in a finite size population is established. The evolutionary process of strategies in the population is described as a multidimensional ergodic and continuous time Markov process. The stochastic stable state of the system is analyzed by the limit distribution of the stochastic process. By numerical experiments, the influences of the fixed income coefficient for non-participants and the investment income coefficient of the public goods on the stochastic stable equilibrium of the system are analyzed. Through the numerical calculation results, we found that the optional participation mechanism can change the evolutionary dynamics and the equilibrium of the public goods game, and there is a range of parameters which can effectively promote the evolution of cooperation. Further, we obtain the accurate quantitative relationship between the parameters and the probabilities for the system to choose different stable equilibriums, which can be used to realize the control of cooperation.

Induction of diploid gynogenesis in an evolutionary model organism, the three-spined stickleback (Gasterosteus aculeatus)

PubMed Central

2011-01-01

Background Rapid advances in genomics have provided nearly complete genome sequences for many different species. However, no matter how the sequencing technology has improved, natural genetic polymorphism complicates the production of high quality reference genomes. To address this problem, researchers have tried using artificial modes of genome manipulation such as gynogenesis for fast production of inbred lines. Results Here, we present the first successful induction of diploid gynogenesis in an evolutionary model system, the three-spined sticklebacks (Gasterosteus aculeatus), using a combination of UV-irradiation of the sperm and heat shock (HS) of the resulting embryo to inhibit the second meiotic division. Optimal UV irradiation of the sperm was established by exposing stickleback sperm to a UV- light source at various times. Heat shock parameters like temperature, duration, and time of initiation were tested by subjecting eggs fertilized with UV inactivated sperm 5, 10, 15, 20, 25, or 30 minutes post fertilization (mpf) to 30°C, 34°C, or 38°C for 2, 4, 6 or 8 minutes. Gynogen yield was highest when stickleback eggs were activated with 2 minutes UV-irradiated sperm and received HS 5 mpf at 34°C for 4 minutes. Conclusions Diploid gynogenesis has been successfully performed in three-spined stickleback. This has been confirmed by microsatellite DNA analysis which revealed exclusively maternal inheritance in all gynogenetic fry tested. Ploidy verification by flow cytometry showed that gynogenetic embryos/larvae exhibiting abnormalities were haploids and those that developed normally were diploids, i.e., double haploids that can be raised until adult size. PMID:21910888

Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis.

PubMed

O'Malley, Maureen A

2012-01-01

Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.

Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence

PubMed Central

McLeish, Tom C. B.

2015-01-01

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity—the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity—essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution. PMID:26640648

Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence.

PubMed

McLeish, Tom C B

2015-12-06

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity-the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity-essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution.

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.

Continuous Glucose Monitoring

MedlinePlus

... costs will be covered. What is an artificial pancreas? A CGM is one part of the “artificial pancreas” systems that are beginning to reach people with ... has played an important role in developing artificial pancreas technology. An artificial pancreas replaces manual blood glucose ...

Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

ERIC Educational Resources Information Center

Buckland, Michael K.; Florian, Doris

1991-01-01

Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

Blood feeding of Ornithodoros turicata larvae using an artificial membrane system

USDA-ARS?s Scientific Manuscript database

An artificial membrane system was adapted to feed Ornithodoros turicata larvae from a laboratory colony using defibrinated swine blood. Aspects related to larval feeding and molting to the 1st nymphal instar were evaluated. Fifty-five percent of all larvae exposed to the artificial membrane in two e...

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

Innovation in Extraterrestrial Service Systems - A Challenge for Service Science

NASA Technical Reports Server (NTRS)

Bergner, David

2010-01-01

This presentation was prepared at the invitation of Professor Yukio Ohsawa, Department of Systems Innovation, School of Engineering, The University of Tokyo, for delivery at the International Workshop on Innovating Service Systems, sponsored by the Japanese Society of Artificial Intelligence (JSAI) as part of the JSAI Internation Symposium on AI, 2010. It offers several challenges for Service Science and Service Innovation. the goal of the presentation is to stimulate thinking about how service systems viII evolve in the future, as human society advances from its terrestrial base toward a permanent presence in space. First we will consider the complexity of the International Space Station (ISS) as it is today, with particular emphasis of its research facilities, and focus on a current challenge - to maximize the utilization of ISS research facilities for the benefit of society. After briefly reviewing the basic principles of Service Science, we will discuss the potential application of Service Innovation methodology to this challenge. Then we viII consider how game-changing technologies - in particular Synthetic Biology - could accelerate the pace of sociocultural evolution and consequently, the progression of human society into space. We will use this provocative vision to advance thinking about how the emerging field of Service Science, Management, and Engineering (SSME) might help us anticipate and better handle the challenges of this inevitable evolutionary process.

Adaptive evolution of body size subject to indirect effect in trophic cascade system.

PubMed

Wang, Xin; Fan, Meng; Hao, Lina

2017-09-01

Trophic cascades represent a classic example of indirect effect and are wide-spread in nature. Their ecological impact are well established, but the evolutionary consequences have received even less theoretical attention. We theoretically and numerically investigate the trait (i.e., body size of consumer) evolution in response to indirect effect in a trophic cascade system. By applying the quantitative trait evolutionary theory and the adaptive dynamic theory, we formulate and explore two different types of eco-evolutionary resource-consumer-predator trophic cascade model. First, an eco-evolutionary model incorporating the rapid evolution is formulated to investigate the effect of rapid evolution of the consumer's body size, and to explore the impact of density-mediate indirect effect on the population dynamics and trait dynamics. Next, by employing the adaptive dynamic theory, a long-term evolutionary model of consumer body size is formulated to evaluate the effect of long-term evolution on the population dynamics and the effect of trait-mediate indirect effect. Those models admit rich dynamics that has not been observed yet in empirical studies. It is found that, both in the trait-mediated and density-mediated system, the body size of consumer in predator-consumer-resource interaction (indirect effect) evolves smaller than that in consumer-resource and predator-consumer interaction (direct effect). Moreover, in the density-mediated system, we found that the evolution of consumer body size contributes to avoiding consumer extinction (i.e., evolutionary rescue). The trait-mediate and density-mediate effects may produce opposite evolutionary response. This study suggests that the trophic cascade indirect effect affects consumer evolution, highlights a more comprehensive mechanistic understanding of the intricate interplay between ecological and evolutionary force. The modeling approaches provide avenue for study on indirect effects from an evolutionary perspective. Copyright © 2017 Elsevier B.V. All rights reserved.

Design of a hydraulic analog of the circulatory system for evaluating artificial hearts.

PubMed

Donovan, F M

1975-01-01

A major problem in improving artificial heart designs is the absence of methods for accurate in vitro testing of artificial heart systems. A mock circulatory system has been constructed which hydraulically simulates the systemic and pulmonary circulations of the normal human. The device is constructed of 1/2 in. acrylic sheet and has overall dimensions of 24 in. wide, 16 in. tall, and 8 in. deep. The artificial heart to be tested is attached to the front of the device, and pumps fluid from the systemic venous chamber into the pulmonary arterial chamber and from the pulmonary venous chamber into the systemic arterial chamber. Each of the four chambers is hermetically sealed. The compliance of each chamber is determined by the volume of air trapped above the fluid in that chamber. The pulmonary and systemic resistances are set automatically by bellows-operated valves to simulate the barroreceptor response in the systemic arteries and the passive pulmonary resistance response in the pulmonary arteries. Cardiac output is measured by a turbine flowmeter in the systemic circulation. Results using the Kwan-Gett artificial heart show a good comparison between the mock circulatory system response and the calf response.

Estimating directional epistasis

PubMed Central

Le Rouzic, Arnaud

2014-01-01

Epistasis, i.e., the fact that gene effects depend on the genetic background, is a direct consequence of the complexity of genetic architectures. Despite this, most of the models used in evolutionary and quantitative genetics pay scant attention to genetic interactions. For instance, the traditional decomposition of genetic effects models epistasis as noise around the evolutionarily-relevant additive effects. Such an approach is only valid if it is assumed that there is no general pattern among interactions—a highly speculative scenario. Systematic interactions generate directional epistasis, which has major evolutionary consequences. In spite of its importance, directional epistasis is rarely measured or reported by quantitative geneticists, not only because its relevance is generally ignored, but also due to the lack of simple, operational, and accessible methods for its estimation. This paper describes conceptual and statistical tools that can be used to estimate directional epistasis from various kinds of data, including QTL mapping results, phenotype measurements in mutants, and artificial selection responses. As an illustration, I measured directional epistasis from a real-life example. I then discuss the interpretation of the estimates, showing how they can be used to draw meaningful biological inferences. PMID:25071828

Implementation and comparative analysis of the optimisations produced by evolutionary algorithms for the parameter extraction of PSP MOSFET model

NASA Astrophysics Data System (ADS)

Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.

2016-05-01

The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.

Artificial Intelligence, DNA Mimicry, and Human Health.

PubMed

Stefano, George B; Kream, Richard M

2017-08-14

The molecular evolution of genomic DNA across diverse plant and animal phyla involved dynamic registrations of sequence modifications to maintain existential homeostasis to increasingly complex patterns of environmental stressors. As an essential corollary, driver effects of positive evolutionary pressure are hypothesized to effect concerted modifications of genomic DNA sequences to meet expanded platforms of regulatory controls for successful implementation of advanced physiological requirements. It is also clearly apparent that preservation of updated registries of advantageous modifications of genomic DNA sequences requires coordinate expansion of convergent cellular proofreading/error correction mechanisms that are encoded by reciprocally modified genomic DNA. Computational expansion of operationally defined DNA memory extends to coordinate modification of coding and previously under-emphasized noncoding regions that now appear to represent essential reservoirs of untapped genetic information amenable to evolutionary driven recruitment into the realm of biologically active domains. Additionally, expansion of DNA memory potential via chemical modification and activation of noncoding sequences is targeted to vertical augmentation and integration of an expanded cadre of transcriptional and epigenetic regulatory factors affecting linear coding of protein amino acid sequences within open reading frames.

Properties of Artifact Representations for Evolutionary Design

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2004-01-01

To achieve evolutionary design systems that scale to the levels achieved by man-made artifacts we can look to their characteristics of modularity, hierarchy and regularity to guide us. For this we focus on design representations, since they strongly determine the ability of evolutionary design systems to evolve artifacts with these characteristics. We identify three properties of design representations - combination, control-flow and abstraction - and discuss how they relate to hierarchy, modularity and regularity.

Incomplete response to artificial tears is associated with features of neuropathic ocular pain.

PubMed

Galor, Anat; Batawi, Hatim; Felix, Elizabeth R; Margolis, Todd P; Sarantopoulos, Konstantinos D; Martin, Eden R; Levitt, Roy C

2016-06-01

Artificial tears are first-line therapy for patients with dry eye symptoms. It is not known, however, which patient factors associate with a positive response to therapy. The purpose of this study was to evaluate whether certain ocular and systemic findings are associated with a differential subjective response to artificial tears. Cross-sectional study of 118 individuals reporting artificial tears use (hypromellose 0.4%) to treat dry eye-associated ocular pain. An evaluation was performed to assess dry eye symptoms (via the dry eye questionnaire 5 and ocular surface disease index), ocular and systemic (non-ocular) pain complaints and ocular signs (tear osmolarity, tear breakup time, corneal staining, Schirmer testing with anaesthesia, and eyelid and meibomian gland assessment). The main outcome measures were factors associated with differential subjective response to artificial tears. By self-report, 23 patients reported no improvement, 73 partial improvement and 22 complete improvement in ocular pain with artificial tears. Patients who reported no or partial improvement in pain with artificial tears reported higher levels of hot-burning ocular pain and sensitivity to wind compared with those with complete improvement. Patients were also asked to rate the intensity of systemic pain elsewhere in the body (other than the eye). Patients who reported no or incomplete improvement with artificial tears had higher systemic pain scores compared with those with complete improvement. Both ocular and systemic (non-ocular) pain complaints are associated with a differential subjective response to artificial tears. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A comparative study on genetic effects of artificial and natural habitat fragmentation on Loropetalum chinense (Hamamelidaceae) in Southeast China.

PubMed

Yuan, N; Comes, H P; Cao, Y N; Guo, R; Zhang, Y H; Qiu, Y X

2015-06-01

Elucidating the demographic and landscape features that determine the genetic effects of habitat fragmentation has become fundamental to research in conservation and evolutionary biology. Land-bridge islands provide ideal study areas for investigating the genetic effects of habitat fragmentation at different temporal and spatial scales. In this context, we compared patterns of nuclear microsatellite variation between insular populations of a shrub of evergreen broad-leaved forest, Loropetalum chinense, from the artificially created Thousand-Island Lake (TIL) and the Holocene-dated Zhoushan Archipelago of Southeast China. Populations from the TIL region harboured higher levels of genetic diversity than those from the Zhoushan Archipelago, but these differences were not significant. There was no correlation between genetic diversity and most island features, excepting a negative effect of mainland-island distance on allelic richness and expected heterozygosity in the Zhoushan Archipelago. In general, levels of gene flow among island populations were moderate to high, and tests of alternative models of population history strongly favoured a gene flow-drift model over a pure drift model in each region. In sum, our results showed no obvious genetic effects of habitat fragmentation due to recent (artificial) or past (natural) island formation. Rather, they highlight the importance of gene flow (most likely via seed) in maintaining genetic variation and preventing inter-population differentiation in the face of habitat 'insularization' at different temporal and spatial scales.

J.A. Schumpeter and T.B. Veblen on economic evolution: the dichotomy between statics and dynamics

PubMed Central

Schütz, Marlies; Rainer, Andreas

2016-01-01

Abstract At present, the discussion on the dichotomy between statics and dynamics is resolved by concentrating on its mathematical meaning. Yet, a simple formalisation masks the underlying methodological discussion. Overcoming this limitation, the paper discusses Schumpeter's and Veblen's viewpoint on dynamic economic systems as systems generating change from within. It contributes to an understanding on their ideas of how economics could become an evolutionary science and on their contributions to elaborate an evolutionary economics. It confronts Schumpeter's with Veblen's perspective on evolutionary economics and provides insight into their evolutionary economic theorising by discussing their ideas on the evolution of capitalism. PMID:28057981

The future orientation of constructive memory: an evolutionary perspective on therapeutic hypnosis and brief psychotherapy.

PubMed

Rossi, Ernest; Erickson-Klein, Roxanna; Rossi, Kathryn

2008-04-01

We explore a new distinction between the future, prospective memory system being investigated in current neuroscience and the past, retrospective memory system, which was the original theoretical foundation of therapeutic hypnosis, classical psychoanalysis, and psychotherapy. We then generalize a current evolutionary theory of sleep and dreaming, which focuses on the future, prospective memory system, to conceptualize a new evolutionary perspective on therapeutic hypnosis and brief psychotherapy. The implication of current neuroscience research is that activity-dependent gene expression and brain plasticity are the psychobiological basis of adaptive behavior, consciousness, and creativity in everyday life as well as psychotherapy. We summarize a case illustrating how this evolutionary perspective can be used to quickly resolve problems with past obstructive procrastination in school to facilitate current and future academic success.

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.

Experimental macroevolution†

PubMed Central

Bell, Graham

2016-01-01

The convergence of several disparate research programmes raises the possibility that the long-term evolutionary processes of innovation and radiation may become amenable to laboratory experimentation. Ancestors might be resurrected directly from naturally stored propagules or tissues, or indirectly from the expression of ancestral genes in contemporary genomes. New kinds of organisms might be evolved through artificial selection of major developmental genes. Adaptive radiation can be studied by mimicking major ecological transitions in the laboratory. All of these possibilities are subject to severe quantitative and qualitative limitations. In some cases, however, laboratory experiments may be capable of illuminating the processes responsible for the evolution of new kinds of organisms. PMID:26763705

Neuro-evolutionary computing paradigm for Painlevé equation-II in nonlinear optics

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Ahmad, Sufyan; Awais, Muhammad; Ul Islam Ahmad, Siraj; Asif Zahoor Raja, Muhammad

2018-05-01

The aim of this study is to investigate the numerical treatment of the Painlevé equation-II arising in physical models of nonlinear optics through artificial intelligence procedures by incorporating a single layer structure of neural networks optimized with genetic algorithms, sequential quadratic programming and active set techniques. We constructed a mathematical model for the nonlinear Painlevé equation-II with the help of networks by defining an error-based cost function in mean square sense. The performance of the proposed technique is validated through statistical analyses by means of the one-way ANOVA test conducted on a dataset generated by a large number of independent runs.

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

PubMed

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Visiting Scholars Program

DTIC Science & Technology

2016-09-01

other associated grants. 15. SUBJECT TERMS SUNY Poly, STEM, Artificial Intelligence , Command and Control 16. SECURITY CLASSIFICATION OF: 17...neuromorphic system has the potential to be widely used in a high-efficiency artificial intelligence system. Simulation results have indicated that the...novel multiresolution fusion and advanced fusion performance evaluation tool for an Artificial Intelligence based natural language annotation engine for

Why an extended evolutionary synthesis is necessary

PubMed Central

2017-01-01

Since the last major theoretical integration in evolutionary biology—the modern synthesis (MS) of the 1940s—the biosciences have made significant advances. The rise of molecular biology and evolutionary developmental biology, the recognition of ecological development, niche construction and multiple inheritance systems, the ‘-omics’ revolution and the science of systems biology, among other developments, have provided a wealth of new knowledge about the factors responsible for evolutionary change. Some of these results are in agreement with the standard theory and others reveal different properties of the evolutionary process. A renewed and extended theoretical synthesis, advocated by several authors in this issue, aims to unite pertinent concepts that emerge from the novel fields with elements of the standard theory. The resulting theoretical framework differs from the latter in its core logic and predictive capacities. Whereas the MS theory and its various amendments concentrate on genetic and adaptive variation in populations, the extended framework emphasizes the role of constructive processes, ecological interactions and systems dynamics in the evolution of organismal complexity as well as its social and cultural conditions. Single-level and unilinear causation is replaced by multilevel and reciprocal causation. Among other consequences, the extended framework overcomes many of the limitations of traditional gene-centric explanation and entails a revised understanding of the role of natural selection in the evolutionary process. All these features stimulate research into new areas of evolutionary biology. PMID:28839929

Interactions between Artificial Gravity, the Affected Physiological Systems, and Nutrition

NASA Technical Reports Server (NTRS)

Heer, Martina; Baecker, Nathalie; Zwart, Sara; Smith, Scott

2006-01-01

Malnutrition, either by insufficient supply of some nutrients or by overfeeding, has a profound effect on the health of an organism. Therefore, optimal nutrition is a necessity in normal gravity on Earth, in microgravity, and when applying artificial gravity to the human system. Reduced physical activity, such as observed in microgravity or bed rest, has an effect on many physiological systems, such as the cardiovascular, musculoskeletal, immune, and body fluids regulation systems. There is currently no countermeasure that is effective to counteract both the cardiovascular and musculoskeletal deconditioning when applied for a short duration (see Chapter 1). Artificial gravity therefore seems the simplest physiological approach to keep these systems intact. The application of intermittent daily dose of artificial gravity by means of centrifugation has often been proposed as a potential countermeasure against the physiological deconditioning induced by spaceflight. However, neither the optimal gravity level, nor its optimal duration of exposure have been enough studied to recommend a validated, effective, and efficient artificial gravity application. As discussed in previous chapters, artificial gravity has a very high potential to counteract any changes caused by reduced physical activity. The nutrient supply, which ideally should match the actual needs, will interact with these changes and therefore has also to be taken into account. This chapter reviews the potential interactions between these nutrients (energy intake, vitamins, minerals) and the other physiological systems affected by artificial gravity generated by an on-board short-radius centrifuge.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis.

PubMed

Cheng, Yezeng; Larin, Kirill V

2006-12-20

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

NASA Astrophysics Data System (ADS)

Cheng, Yezeng; Larin, Kirill V.

2006-12-01

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

NASA Astrophysics Data System (ADS)

Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

2018-03-01

The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

Memory and learning behaviors mimicked in nanogranular SiO2-based proton conductor gated oxide-based synaptic transistors

NASA Astrophysics Data System (ADS)

Wan, Chang Jin; Zhu, Li Qiang; Zhou, Ju Mei; Shi, Yi; Wan, Qing

2013-10-01

In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements.In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr02987e

Development of haptic based piezoresistive artificial fingertip: Toward efficient tactile sensing systems for humanoids.

PubMed

TermehYousefi, Amin; Azhari, Saman; Khajeh, Amin; Hamidon, Mohd Nizar; Tanaka, Hirofumi

2017-08-01

Haptic sensors are essential devices that facilitate human-like sensing systems such as implantable medical devices and humanoid robots. The availability of conducting thin films with haptic properties could lead to the development of tactile sensing systems that stretch reversibly, sense pressure (not just touch), and integrate with collapsible. In this study, a nanocomposite based hemispherical artificial fingertip fabricated to enhance the tactile sensing systems of humanoid robots. To validate the hypothesis, proposed method was used in the robot-like finger system to classify the ripe and unripe tomato by recording the metabolic growth of the tomato as a function of resistivity change during a controlled indention force. Prior to fabrication, a finite element modeling (FEM) was investigated for tomato to obtain the stress distribution and failure point of tomato by applying different external loads. Then, the extracted computational analysis information was utilized to design and fabricate nanocomposite based artificial fingertip to examine the maturity analysis of tomato. The obtained results demonstrate that the fabricated conformable and scalable artificial fingertip shows different electrical property for ripe and unripe tomato. The artificial fingertip is compatible with the development of brain-like systems for artificial skin by obtaining periodic response during an applied load. Copyright © 2017. Published by Elsevier B.V.

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

PubMed Central

Liu, Yang; Liu, Junfei

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency. PMID:27725826

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation.

PubMed

Liu, Yang; Liu, Junfei; Tian, Liwei; Ma, Lianbo

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

Artificial Autopolyploidization Modifies the Tricarboxylic Acid Cycle and GABA Shunt in Arabidopsis thaliana Col-0

NASA Astrophysics Data System (ADS)

Vergara, Fredd; Kikuchi, Jun; Breuer, Christian

2016-05-01

Autopolyploidy is a process whereby the chromosome set is multiplied and it is a common phenomenon in angiosperms. Autopolyploidy is thought to be an important evolutionary force that has led to the formation of new plant species. Despite its relevance, the consequences of autopolyploidy in plant metabolism are poorly understood. This study compares the metabolic profiles of natural diploids and artificial autotetraploids of Arabidopsis thaliana Col-0. Different physiological parameters are compared between diploids and autotetraploids using nuclear magnetic resonance (NMR), elemental analysis (carbon:nitrogen balance) and quantitative real-time PCR (qRT-PCR). The main difference between diploid and autotetraploid A. thaliana Col-0 is observed in the concentration of metabolites related to the tricarboxylic acid cycle (TCA) and γ-amino butyric acid (GABA) shunt, as shown by multivariate statistical analysis of NMR spectra. qRT-PCR shows that genes related to the TCA and GABA shunt are also differentially expressed between diploids and autotetraploids following similar trends as their corresponding metabolites. Solid evidence is presented to demonstrate that autopolyploidy influences core plant metabolic processes.

Morphological integration in the appendicular skeleton of two domestic taxa: the horse and donkey.

PubMed

Hanot, Pauline; Herrel, Anthony; Guintard, Claude; Cornette, Raphaël

2017-10-11

Organisms are organized into suites of anatomical structures that typically covary when developmentally or functionally related, and this morphological integration plays a determinant role in evolutionary processes. Artificial selection on domestic species causes strong morphological changes over short time spans, frequently resulting in a wide and exaggerated phenotypic diversity. This raises the question of whether integration constrains the morphological diversification of domestic species and how natural and artificial selection may impact integration patterns. Here, we study the morphological integration in the appendicular skeleton of domestic horses and donkeys, using three-dimensional geometric morphometrics on 75 skeletons. Our results indicate that a strong integration is inherited from developmental mechanisms which interact with functional factors. This strong integration reveals a specialization in the locomotion of domestic equids, partly for running abilities. We show that the integration is stronger in horses than in donkeys, probably because of a greater degree of specialization and predictability of their locomotion. Thus, the constraints imposed by integration are weak enough to allow important morphological changes and the phenotypic diversification of domestic species. © 2017 The Author(s).

An evolutionary perspective on health psychology: new approaches and applications.

PubMed

Tybur, Joshua M; Bryan, Angela D; Hooper, Ann E Caldwell

2012-12-20

Although health psychologists' efforts to understand and promote health are most effective when guided by theory, health psychology has not taken full advantage of theoretical insights provided by evolutionary psychology. Here, we argue that evolutionary perspectives can fruitfully inform strategies for addressing some of the challenges facing health psychologists. Evolutionary psychology's emphasis on modular, functionally specialized psychological systems can inform approaches to understanding the myriad behaviors grouped under the umbrella of "health," as can theoretical perspectives used by evolutionary anthropologists, biologists, and psychologists (e.g., Life History Theory). We detail some early investigations into evolutionary health psychology, and we provide suggestions for directions for future research.

Single Microwave-Photon Detector using an Artificial Lambda-type Three-Level System

DTIC Science & Technology

2016-01-11

Single microwave-photon detector using an artificial Λ-type three- level system Kunihiro Inomata,1∗†, Zhirong Lin,1†, Kazuki Koshino,2, William D...three- level system Kunihiro Inomata,1∗† Zhirong Lin,1† Kazuki Koshino,2 William D. Oliver,3,4 Jaw-Shen Tsai,1 Tsuyoshi Yamamoto,5 Yasunobu Nakamura...single-microwave-photon detector based on the deterministic switching in an artificial Λ-type three- level system implemented using the dressed states of a

Progress in cybernetics and systems research. Vol. XI. Data base design. International Information Systems. Semiotic Systems. Artificial Intelligence. Cybernetics and Philosophy. Special aspects

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

Trappl, R.; Findler, N.V.; Horn, W.

1982-01-01

This book covers current research topics in six areas. These are data base design, international information systems, semiotic systems, artificial intelligence, cybernetics and philosophy, and special aspects of systems research. 1326 references.

Methodology Investigation of AI(Artificial Intelligence) Test Officer Support Tool. Volume 1

DTIC Science & Technology

1989-03-01

American Association for Artificial inteligence A! ............. Artificial inteliigence AMC ............ Unt:ed States Army Maeriel Comand ASL...block number) FIELD GROUP SUB-GROUP Artificial Intelligence, Expert Systems Automated Aids to Testing 9. ABSTRACT (Continue on reverse if necessary and...identify by block number) This report covers the application of Artificial Intelligence-Techniques to the problem of creating automated tools to

Modeling the stylized facts in finance through simple nonlinear adaptive systems

PubMed Central

Hommes, Cars H.

2002-01-01

Recent work on adaptive systems for modeling financial markets is discussed. Financial markets are viewed as evolutionary systems between different, competing trading strategies. Agents are boundedly rational in the sense that they tend to follow strategies that have performed well, according to realized profits or accumulated wealth, in the recent past. Simple technical trading rules may survive evolutionary competition in a heterogeneous world where prices and beliefs co-evolve over time. Evolutionary models can explain important stylized facts, such as fat tails, clustered volatility, and long memory, of real financial series. PMID:12011401

Signatures of selection acting on the innate immunity gene Toll-like receptor 2 (TLR2) during the evolutionary history of rodents.

PubMed

Tschirren, B; Råberg, L; Westerdahl, H

2011-06-01

Patterns of selection acting on immune defence genes have recently been the focus of considerable interest. Yet, when it comes to vertebrates, studies have mainly focused on the acquired branch of the immune system. Consequently, the direction and strength of selection acting on genes of the vertebrate innate immune defence remain poorly understood. Here, we present a molecular analysis of selection on an important receptor of the innate immune system of vertebrates, the Toll-like receptor 2 (TLR2), across 17 rodent species. Although purifying selection was the prevalent evolutionary force acting on most parts of the rodent TLR2, we found that codons in close proximity to pathogen-binding and TLR2-TLR1 heterodimerization sites have been subject to positive selection. This indicates that parasite-mediated selection is not restricted to acquired immune system genes like the major histocompatibility complex, but also affects innate defence genes. To obtain a comprehensive understanding of evolutionary processes in host-parasite systems, both innate and acquired immunity thus need to be considered. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

Phenotypic engineering unveils the function of genital morphology.

PubMed

Hotzy, Cosima; Polak, Michal; Rönn, Johanna L; Arnqvist, Göran

2012-12-04

The rapidly evolving and often extraordinarily complex appearance of male genital morphology of internally fertilizing animals has been recognized for centuries. Postcopulatory sexual selection is regarded as the likely evolutionary engine of this diversity, but direct support for this hypothesis is limited. We used two complementary approaches, evolution through artificial selection and microscale laser surgery, to experimentally manipulate genital morphology in an insect model system. We then assessed the competitive fertilization success of these phenotypically manipulated males and studied the fate of their ejaculate in females using high-resolution radioisotopic labeling of ejaculates. Males with longer genital spines were more successful in gaining fertilizations, providing experimental evidence that male genital morphology influences success in postcopulatory reproductive competition. Furthermore, a larger proportion of the ejaculate moved from the reproductive tract into the female body following mating with males with longer spines, suggesting that genital spines increase the rate at which seminal fluid passes into the female hemolymph. Our results show that genital morphology affects male competitive fertilization success and imply that sexual selection on genital morphology may be mediated in part through seminal fluid. Copyright © 2012 Elsevier Ltd. All rights reserved.

Hidden Markov models and other machine learning approaches in computational molecular biology

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

Baldi, P.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

DTIC Science & Technology

1991-12-01

neural network and the feedforward neural network studied is the single layer perceptron artificial neural network . The recurrent artificial neural network input...features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input

Protecting Networks Via Automated Defense of Cyber Systems

DTIC Science & Technology

2016-09-01

autonomics, and artificial intelligence . Our conclusion is that automation is the future of cyber defense, and that advances are being made in each of...SUBJECT TERMS Internet of Things, autonomics, sensors, artificial intelligence , cyber defense, active cyber defense, automated indicator sharing...called Automated Defense of Cyber Systems, built upon three core technological components: sensors, autonomics, and artificial intelligence . Our

Optical Inference Machines

DTIC Science & Technology

1988-06-27

de olf nessse end Id e ;-tl Sb ieeI smleo) ,Optical Artificial Intellegence ; Optical inference engines; Optical logic; Optical informationprocessing...common. They arise in areas such as expert systems and other artificial intelligence systems. In recent years, the computer science language PROLOG has...cal processors should in principle be well suited for : I artificial intelligence applications. In recent years, symbolic logic processing. , the

Testing of Safety-Critical Software Embedded in an Artificial Heart

NASA Astrophysics Data System (ADS)

Cha, Sungdeok; Jeong, Sehun; Yoo, Junbeom; Kim, Young-Gab

Software is being used more frequently to control medical devices such as artificial heart or robotic surgery system. While much of software safety issues in such systems are similar to other safety-critical systems (e.g., nuclear power plants), domain-specific properties may warrant development of customized techniques to demonstrate fitness of the system on patients. In this paper, we report results of a preliminary analysis done on software controlling a Hybrid Ventricular Assist Device (H-VAD) developed by Korea Artificial Organ Centre (KAOC). It is a state-of-the-art artificial heart which completed animal testing phase. We performed software testing in in-vitro experiments and animal experiments. An abnormal behaviour, never detected during extensive in-vitro analysis and animal testing, was found.

A photocatalyst-enzyme coupled artificial photosynthesis system for solar energy in production of formic acid from CO2.

PubMed

Yadav, Rajesh K; Baeg, Jin-Ook; Oh, Gyu Hwan; Park, No-Joong; Kong, Ki-jeong; Kim, Jinheung; Hwang, Dong Won; Biswas, Soumya K

2012-07-18

The photocatalyst-enzyme coupled system for artificial photosynthesis process is one of the most promising methods of solar energy conversion for the synthesis of organic chemicals or fuel. Here we report the synthesis of a novel graphene-based visible light active photocatalyst which covalently bonded the chromophore, such as multianthraquinone substituted porphyrin with the chemically converted graphene as a photocatalyst of the artificial photosynthesis system for an efficient photosynthetic production of formic acid from CO(2). The results not only show a benchmark example of the graphene-based material used as a photocatalyst in general artificial photosynthesis but also the benchmark example of the selective production system of solar chemicals/solar fuel directly from CO(2).

Buried treasure: evolutionary perspectives on microbial iron piracy

PubMed Central

Barber, Matthew F.; Elde, Nels C.

2015-01-01

Host-pathogen interactions provide valuable systems for the study of evolutionary genetics and natural selection. The sequestration of essential iron has emerged as a critical innate defense system termed nutritional immunity, leading pathogens to evolve mechanisms of `iron piracy' to scavenge this metal from host proteins. This battle for iron carries numerous consequences not only for host-pathogen evolution, but also microbial community interactions. Here we highlight recent and potential future areas of investigation on the evolutionary implications of microbial iron piracy in relation to molecular arms races, host range, competition, and virulence. Applying evolutionary genetic approaches to the study of microbial iron acquisition could also provide new inroads for understanding and combating infectious disease. PMID:26431675

A Genetic Representation for Evolutionary Fault Recovery in Virtex FPGAs

NASA Technical Reports Server (NTRS)

Lohn, Jason; Larchev, Greg; DeMara, Ronald; Korsmeyer, David (Technical Monitor)

2003-01-01

Most evolutionary approaches to fault recovery in FPGAs focus on evolving alternative logic configurations as opposed to evolving the intra-cell routing. Since the majority of transistors in a typical FPGA are dedicated to interconnect, nearly 80% according to one estimate, evolutionary fault-recovery systems should benefit hy accommodating routing. In this paper, we propose an evolutionary fault-recovery system employing a genetic representation that takes into account both logic and routing configurations. Experiments were run using a software model of the Xilinx Virtex FPGA. We report that using four Virtex combinational logic blocks, we were able to evolve a 100% accurate quadrature decoder finite state machine in the presence of a stuck-at-zero fault.

50 CFR 27.73 - Artificial lights.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 50 Wildlife and Fisheries 6 2010-10-01 2010-10-01 false Artificial lights. 27.73 Section 27.73... NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Light and Sound Equipment § 27.73 Artificial lights. No unauthorized person shall use or direct the rays of a spotlight or other artificial...

50 CFR 27.73 - Artificial lights.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 50 Wildlife and Fisheries 9 2012-10-01 2012-10-01 false Artificial lights. 27.73 Section 27.73... NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Light and Sound Equipment § 27.73 Artificial lights. No unauthorized person shall use or direct the rays of a spotlight or other artificial...

50 CFR 27.73 - Artificial lights.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 50 Wildlife and Fisheries 8 2011-10-01 2011-10-01 false Artificial lights. 27.73 Section 27.73... NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Light and Sound Equipment § 27.73 Artificial lights. No unauthorized person shall use or direct the rays of a spotlight or other artificial...

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability

USGS Publications Warehouse

Simon, Troy N.; Bassar, Ronald D.; Binderup, Andrew J.; Flecker, Alex S.; Freeman, Mary C.; Gilliam, James F.; Marshall, Michael C.; Thomas, Steve A.; Travis, Joseph; Reznick, David N.; Pringle, Catherine M.

2017-01-01

While previous studies have shown that evolutionary divergence alters ecological processes in small-scale experiments, a major challenge is to assess whether such evolutionary effects are important in natural ecosystems at larger spatial scales. At the landscape scale, across eight streams in the Caroni drainage, we found that the presence of locally adapted populations of guppies (Poecilia reticulata) is associated with reduced algal biomass and increased invertebrate biomass, while the opposite trends were true in streams with experimentally introduced populations of non-locally adapted guppies. Exclusion experiments conducted in two separate reaches of a single stream showed that guppies with locally adapted phenotypes significantly reduced algae with no effect on invertebrates, while non-adapted guppies had no effect on algae but significantly reduced invertebrates. These divergent effects of phenotype on stream ecosystems are comparable in strength to the effects of abiotic factors (e.g., light) known to be important drivers of ecosystem condition. They also corroborate the results of previous experiments conducted in artificial streams. Our results demonstrate that local adaptation can produce phenotypes with significantly different effects in natural ecosystems at a landscape scale, within a tropical watershed, despite high variability in abiotic factors: five of the seven physical and chemical parameters measured across the eight study streams varied by more than one order of magnitude. Our findings suggest that ecosystem structure is, in part, an evolutionary product and not simply an ecological pattern.

Evolutionary genetics of plant adaptation.

PubMed

Anderson, Jill T; Willis, John H; Mitchell-Olds, Thomas

2011-07-01

Plants provide unique opportunities to study the mechanistic basis and evolutionary processes of adaptation to diverse environmental conditions. Complementary laboratory and field experiments are important for testing hypotheses reflecting long-term ecological and evolutionary history. For example, these approaches can infer whether local adaptation results from genetic tradeoffs (antagonistic pleiotropy), where native alleles are best adapted to local conditions, or if local adaptation is caused by conditional neutrality at many loci, where alleles show fitness differences in one environment, but not in a contrasting environment. Ecological genetics in natural populations of perennial or outcrossing plants can also differ substantially from model systems. In this review of the evolutionary genetics of plant adaptation, we emphasize the importance of field studies for understanding the evolutionary dynamics of model and nonmodel systems, highlight a key life history trait (flowering time) and discuss emerging conservation issues. Copyright © 2011 Elsevier Ltd. All rights reserved.

The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective

PubMed Central

Billingsley, Joseph; Losin, Elizabeth A. R.

2017-01-01

Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model’s claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part. PMID:28539904

Applying Evolutionary Genetics to Developmental Toxicology and Risk Assessment

PubMed Central

Leung, Maxwell C. K.; Procter, Andrew C.; Goldstone, Jared V.; Foox, Jonathan; DeSalle, Robert; Mattingly, Carolyn J.; Siddall, Mark E.; Timme-Laragy, Alicia R.

2018-01-01

Evolutionary thinking continues to challenge our views on health and disease. Yet, there is a communication gap between evolutionary biologists and toxicologists in recognizing the connections among developmental pathways, high-throughput screening, and birth defects in humans. To increase our capability in identifying potential developmental toxicants in humans, we propose to apply evolutionary genetics to improve the experimental design and data interpretation with various in vitro and whole-organism models. We review five molecular systems of stress response and update 18 consensual cell-cell signaling pathways that are the hallmark for early development, organogenesis, and differentiation; and revisit the principles of teratology in light of recent advances in high-throughput screening, big data techniques, and systems toxicology. Multiscale systems modeling plays an integral role in the evolutionary approach to cross-species extrapolation. Phylogenetic analysis and comparative bioinformatics are both valuable tools in identifying and validating the molecular initiating events that account for adverse developmental outcomes in humans. The discordance of susceptibility between test species and humans (ontogeny) reflects their differences in evolutionary history (phylogeny). This synthesis not only can lead to novel applications in developmental toxicity and risk assessment, but also can pave the way for applying an evo-devo perspective to the study of developmental origins of health and disease. PMID:28267574

An evolutionary advantage for extravagant honesty.

PubMed

Bullock, Seth

2012-01-07

A game-theoretic model of handicap signalling over a pair of signalling channels is introduced in order to determine when one channel has an evolutionary advantage over the other. The stability conditions for honest handicap signalling are presented for a single channel and are shown to conform with the results of prior handicap signalling models. Evolutionary simulations are then used to show that, for a two-channel system in which honest signalling is possible on both channels, the channel featuring larger advertisements at equilibrium is favoured by evolution. This result helps to address a significant tension in the handicap principle literature. While the original theory was motivated by the prevalence of extravagant natural signalling, contemporary models have demonstrated that it is the cost associated with deception that stabilises honesty, and that the honest signals exhibited at equilibrium need not be extravagant at all. The current model suggests that while extravagant and wasteful signals are not required to ensure a signalling system's evolutionary stability, extravagant signalling systems may enjoy an advantage in terms of evolutionary attainability. Copyright © 2011 Elsevier Ltd. All rights reserved.

Emergent geometric frustration of artificial magnetic skyrmion crystals

DOE PAGES

Ma, Fusheng; Reichhardt, Charles; Gan, Weiliang; ...

2016-10-05

Magnetic skyrmions have been receiving growing attention as potential information storage and magnetic logic devices since an increasing number of materials have been identified that support skyrmion phases. Explorations of artificial frustrated systems have led to new insights into controlling and engineering new emergent frustration phenomena in frustrated and disordered systems. Here, we propose a skyrmion spin ice, giving a unifying framework for the study of geometric frustration of skyrmion crystals (SCs) in a nonfrustrated artificial geometrical lattice as a consequence of the structural confinement of skyrmions in magnetic potential wells. The emergent ice rules from the geometrically frustrated SCsmore » highlight a novel phenomenon in this skyrmion system: emergent geometrical frustration. We demonstrate how SC topology transitions between a nonfrustrated periodic configuration and a frustrated icelike ordering can also be realized reversibly. The proposed artificial frustrated skyrmion systems can be annealed into different ice phases with an applied current-induced spin-transfer torque, including a long-range ordered ice rule obeying ground state, as-relaxed random state, biased state, and monopole state. In conclusion, the spin-torque reconfigurability of the artificial skyrmion ice states, difficult to achieve in other artificial spin ice systems, is compatible with standard spintronic device fabrication technology, which makes the semiconductor industrial integration straightforward.« less

Emergent geometric frustration of artificial magnetic skyrmion crystals

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

Ma, Fusheng; Reichhardt, Charles; Gan, Weiliang

Magnetic skyrmions have been receiving growing attention as potential information storage and magnetic logic devices since an increasing number of materials have been identified that support skyrmion phases. Explorations of artificial frustrated systems have led to new insights into controlling and engineering new emergent frustration phenomena in frustrated and disordered systems. Here, we propose a skyrmion spin ice, giving a unifying framework for the study of geometric frustration of skyrmion crystals (SCs) in a nonfrustrated artificial geometrical lattice as a consequence of the structural confinement of skyrmions in magnetic potential wells. The emergent ice rules from the geometrically frustrated SCsmore » highlight a novel phenomenon in this skyrmion system: emergent geometrical frustration. We demonstrate how SC topology transitions between a nonfrustrated periodic configuration and a frustrated icelike ordering can also be realized reversibly. The proposed artificial frustrated skyrmion systems can be annealed into different ice phases with an applied current-induced spin-transfer torque, including a long-range ordered ice rule obeying ground state, as-relaxed random state, biased state, and monopole state. In conclusion, the spin-torque reconfigurability of the artificial skyrmion ice states, difficult to achieve in other artificial spin ice systems, is compatible with standard spintronic device fabrication technology, which makes the semiconductor industrial integration straightforward.« less

Computational aerodynamics and artificial intelligence

NASA Technical Reports Server (NTRS)

Mehta, U. B.; Kutler, P.

1984-01-01

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Design and performance of heart assist or artificial heart control systems

NASA Technical Reports Server (NTRS)

Webb, J. A., Jr.; Gebben, V. D.

1978-01-01

The factors leading to the design of a controlled driving system for either a heart assist pump or artificial heart are discussed. The system provides square pressure waveform to drive a pneumatic-type blood pump. For assist usage the system uses an R-wave detector circuit that can detect the R-wave of the electrocardiogram in the presence of electrical disturbances. This circuit provides a signal useful for synchronizing an assist pump with the natural heart. It synchronizes a square wave circuit, the output of which is converted into square waveforms of pneumatic pressure suitable for driving both assist device and artificial heart. The pressure levels of the driving waveforms are controlled by means of feedback channels to maintain physiological regulation of the artificial heart's output flow. A more compact system that could achieve similar regulatory characteristics is also discussed.

Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

DTIC Science & Technology

1988-04-13

Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

Enhancing Safety of Artificially Ventilated Patients Using Ambient Process Analysis.

PubMed

Lins, Christian; Gerka, Alexander; Lüpkes, Christian; Röhrig, Rainer; Hein, Andreas

2018-01-01

In this paper, we present an approach for enhancing the safety of artificially ventilated patients using ambient process analysis. We propose to use an analysis system consisting of low-cost ambient sensors such as power sensor, RGB-D sensor, passage detector, and matrix infrared temperature sensor to reduce risks for artificially ventilated patients in both home and clinical environments. We describe the system concept and our implementation and show how the system can contribute to patient safety.

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1983-01-01

Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered.

ENGINEERING ECONOMIC ANALYSIS OF A PROGRAM FOR ARTIFICIAL GROUNDWATER RECHARGE.

USGS Publications Warehouse

Reichard, Eric G.; Bredehoeft, John D.

1984-01-01

This study describes and demonstrates two alternate methods for evaluating the relative costs and benefits of artificial groundwater recharge using percolation ponds. The first analysis considers the benefits to be the reduction of pumping lifts and land subsidence; the second considers benefits as the alternative costs of a comparable surface delivery system. Example computations are carried out for an existing artificial recharge program in Santa Clara Valley in California. A computer groundwater model is used to estimate both the average long term and the drought period effects of artificial recharge in the study area. Results indicate that the costs of artificial recharge are considerably smaller than the alternative costs of an equivalent surface system. Refs.

Strategies for Efficient Charge Separation and Transfer in Artificial Photosynthesis of Solar Fuels.

PubMed

Xu, Yuxing; Li, Ailong; Yao, Tingting; Ma, Changtong; Zhang, Xianwen; Shah, Jafar Hussain; Han, Hongxian

2017-11-23

Converting sunlight to solar fuels by artificial photosynthesis is an innovative science and technology for renewable energy. Light harvesting, photogenerated charge separation and transfer (CST), and catalytic reactions are the three primary steps in the processes involved in the conversion of solar energy to chemical energy (SE-CE). Among the processes, CST is the key "energy pump and delivery" step in determining the overall solar-energy conversion efficiency. Efficient CST is always high priority in designing and assembling artificial photosynthesis systems for solar-fuel production. This Review not only introduces the fundamental strategies for CST but also the combinatory application of these strategies to five types of the most-investigated semiconductor-based artificial photosynthesis systems: particulate, Z-scheme, hybrid, photoelectrochemical, and photovoltaics-assisted systems. We show that artificial photosynthesis systems with high SE-CE efficiency can be rationally designed and constructed through combinatory application of these strategies, setting a promising blueprint for the future of solar fuels. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Cyber Situational Awareness Model for Network Administrators

DTIC Science & Technology

2017-03-01

environments, the Internet of Things, artificial intelligence , and so on. As users’ data requirements grow more complex, they demand information...security of systems of interest. Further, artificial intelligence is a powerful concept in information technology. Therefore, new research should...look into how to use artificial intelligence to develop CSA. Human interaction with cyber systems is not making networks and their components safer

Operations Monitoring Assistant System Design

DTIC Science & Technology

1986-07-01

Logic. Artificial Inteligence 25(1)::75-94. January.18. 41 -Nils J. Nilsson. Problem-Solving Methods In Artificli Intelligence. .klcG raw-Hill B3ook...operations monitoring assistant (OMA) system is designed that combines operations research, artificial intelligence, and human reasoning techniques and...KnowledgeCraft (from Carnegie Group), and 5.1 (from Teknowledze). These tools incorporate the best methods of applied artificial intelligence, and

Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

NASA Astrophysics Data System (ADS)

Salamatova, T.; Zhukov, V.

2017-02-01

The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

An automated diagnosis system of liver disease using artificial immune and genetic algorithms.

PubMed

Liang, Chunlin; Peng, Lingxi

2013-04-01

The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

Signal acquisition and analysis for cortical control of neuroprosthetics.

PubMed

Tillery, Stephen I Helms; Taylor, Dawn M

2004-12-01

Work in cortically controlled neuroprosthetic systems has concentrated on decoding natural behaviors from neural activity, with the idea that if the behavior could be fully decoded it could be duplicated using an artificial system. Initial estimates from this approach suggested that a high-fidelity signal comprised of many hundreds of neurons would be required to control a neuroprosthetic system successfully. However, recent studies are showing hints that these systems can be controlled effectively using only a few tens of neurons. Attempting to decode the pre-existing relationship between neural activity and natural behavior is not nearly as important as choosing a decoding scheme that can be more readily deployed and trained to generate the desired actions of the artificial system. These artificial systems need not resemble or behave similarly to any natural biological system. Effective matching of discrete and continuous neural command signals to appropriately configured device functions will enable effective control of both natural and abstract artificial systems using compatible thought processes.

The Principle of Stasis: Why drift is not a Zero-Cause Law.

PubMed

Luque, Victor J

2016-06-01

This paper analyses the structure of evolutionary theory as a quasi-Newtonian theory and the need to establish a Zero-Cause Law. Several authors have postulated that the special character of drift is because it is the default behaviour or Zero-Cause Law of evolutionary systems, where change and not stasis is the normal state of them. For these authors, drift would be a Zero-Cause Law, the default behaviour and therefore a constituent assumption impossible to change without changing the system. I defend that drift's causal and explanatory power prevents it from being considered as a Zero-Cause Law. Instead, I propose that the default behaviour of evolutionary systems is what I call the Principle of Stasis, which posits that an evolutionary system where there is no selection, drift, mutation, migration, etc., and therefore no difference-maker, will not undergo any change (it will remain in stasis). Copyright © 2016 Elsevier Ltd. All rights reserved.

Utilization of artificial intelligence techniques for the Space Station power system

NASA Technical Reports Server (NTRS)

Evatt, Thomas C.; Gholdston, Edward W.

1988-01-01

Due to the complexity of the Space Station Electrical Power System (EPS) as currently envisioned, artificial intelligence/expert system techniques are being investigated to automate operations, maintenance, and diagnostic functions. A study was conducted to investigate this technology as it applies to failure detection, isolation, and reconfiguration (FDIR) and health monitoring of power system components and of the total system. Control system utilization of expert systems for load scheduling and shedding operations was also researched. A discussion of the utilization of artificial intelligence/expert systems for Initial Operating Capability (IOC) for the Space Station effort is presented along with future plans at Rocketdyne for the utilization of this technology for enhanced Space Station power capability.

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

A Study of Driver's Route Choice Behavior Based on Evolutionary Game Theory

PubMed Central

Jiang, Xiaowei; Ji, Yanjie; Deng, Wei

2014-01-01

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent. PMID:25610455

A study of driver's route choice behavior based on evolutionary game theory.

PubMed

Jiang, Xiaowei; Ji, Yanjie; Du, Muqing; Deng, Wei

2014-01-01

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent.

Nanoporous biomaterials for uremic toxin adsorption in artificial kidney systems: A review.

PubMed

Cheah, Wee-Keat; Ishikawa, Kunio; Othman, Radzali; Yeoh, Fei-Yee

2017-07-01

Hemodialysis, one of the earliest artificial kidney systems, removes uremic toxins via diffusion through a semipermeable porous membrane into the dialysate fluid. Miniaturization of the present hemodialysis system into a portable and wearable device to maintain continuous removal of uremic toxins would require that the amount of dialysate used within a closed-system is greatly reduced. Diffused uremic toxins within a closed-system dialysate need to be removed to maintain the optimum concentration gradient for continuous uremic toxin removal by the dialyzer. In this dialysate regenerative system, adsorption of uremic toxins by nanoporous biomaterials is essential. Throughout the years of artificial kidney development, activated carbon has been identified as a potential adsorbent for uremic toxins. Adsorption of uremic toxins necessitates nanoporous biomaterials, especially activated carbon. Nanoporous biomaterials are also utilized in hemoperfusion for uremic toxin removal. Further miniaturization of artificial kidney system and improvements on uremic toxin adsorption capacity would require high performance nanoporous biomaterials which possess not only higher surface area, controlled pore size, but also designed architecture or structure and surface functional groups. This article reviews on various nanoporous biomaterials used in current artificial kidney systems and several emerging nanoporous biomaterials. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1232-1240, 2017. © 2016 Wiley Periodicals, Inc.

Evolution of a designless nanoparticle network into reconfigurable Boolean logic

NASA Astrophysics Data System (ADS)

Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.

2015-12-01

Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

Several necessary conditions for the evolution of complex forms of life in an artificial environment.

PubMed

Suzuki, Hideaki; Ono, Naoaki; Yuta, Kikuo

2003-01-01

In order for an artificial life (Alife) system to evolve complex creatures, an artificial environment prepared by a designer has to satisfy several conditions. To clarify this requirement, we first assume that an artificial environment implemented in the computational medium is composed of an information space in which elementary symbols move around and react with each other according to human-prepared elementary rules. As fundamental properties of these factors (space, symbols, transportation, and reaction), we present ten criteria from a comparison with the biochemical reaction space in the real world. Then, in the latter half of the article, we take several computational Alife systems one by one, and assess them in terms of the proposed criteria. The assessment can be used not only for improving previous Alife systems but also for devising new Alife models in which complex forms of artificial creatures can be expected to evolve.

Engineering artificial cells by combining HeLa-based cell-free expression and ultra-thin double emulsion template

PubMed Central

Ho, Kwun Yin; Murray, Victoria L.; Liu, Allen P.

2015-01-01

Generation of artificial cells provides the bridge needed to cover the gap between studying the complexity of biological processes in whole cells and studying these same processes in an in vitro reconstituted system. Artificial cells are defined as the encapsulation of biologically active material in a biological or synthetic membrane. Here, we describe a robust and general method to produce artificial cells for the purpose of mimicking one or more behaviors of a cell. A microfluidic double emulsion system is used to encapsulate a mammalian cell free expression system that is able to express membrane proteins into the bilayer or soluble proteins inside the vesicles. The development of a robust platform that allows the assembly of artificial cells is valuable in understanding subcellular functions and emergent behaviors in a more cell-like environment as well as for creating novel signaling pathways to achieve specific cellular behaviors. PMID:25997354

From wide to close binaries?

NASA Astrophysics Data System (ADS)

Eggleton, Peter P.

The mechanisms by which the periods of wide binaries (mass 8 solar mass or less and period 10-3000 d) are lengthened or shortened are discussed, synthesizing the results of recent theoretical investigations. A system of nomenclature involving seven evolutionary states, three geometrical states, and 10 types of orbital-period evolution is developed and applied; classifications of 71 binaries are presented in a table along with the basic observational parameters. Evolutionary processes in wide binaries (single-star-type winds, magnetic braking with tidal friction, and companion-reinforced attrition), late case B systems, low-mass X-ray binaries, and triple systems are examined in detail, and possible evolutionary paths are shown in diagrams.

An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system.

PubMed

Oohashi, Tsutomu; Ueno, Osamu; Maekawa, Tadao; Kawai, Norie; Nishina, Emi; Honda, Manabu

2009-01-01

Under the AChem paradigm and the programmed self-decomposition (PSD) model, we propose a hierarchical model for the biomolecular covalent bond (HBCB model). This model assumes that terrestrial organisms arrange their biomolecules in a hierarchical structure according to the energy strength of their covalent bonds. It also assumes that they have evolutionarily selected the PSD mechanism of turning biological polymers (BPs) into biological monomers (BMs) as an efficient biomolecular recycling strategy We have examined the validity and effectiveness of the HBCB model by coordinating two complementary approaches: biological experiments using existent terrestrial life, and simulation experiments using an AChem system. Biological experiments have shown that terrestrial life possesses a PSD mechanism as an endergonic, genetically regulated process and that hydrolysis, which decomposes a BP into BMs, is one of the main processes of such a mechanism. In simulation experiments, we compared different virtual self-decomposition processes. The virtual species in which the self-decomposition process mainly involved covalent bond cleavage from a BP to BMs showed evolutionary superiority over other species in which the self-decomposition process involved cleavage from BP to classes lower than BM. These converging findings strongly support the existence of PSD and the validity and effectiveness of the HBCB model.

Biomimetic molecular design tools that learn, evolve, and adapt.

PubMed

Winkler, David A

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

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

Thangavelautham, Jekanthan; Smith, Alexander; Abu El Samid, Nader

Automation of site preparation and resource utilization on the Moon with teams of autonomous robots holds considerable promise for establishing a lunar base. Such multirobot autonomous systems would require limited human support infrastructure, complement necessary manned operations and reduce overall mission risk. We present an Artificial Neural Tissue (ANT) architecture as a control system for autonomous multirobot excavation tasks. An ANT approach requires much less human supervision and pre-programmed human expertise than previous techniques. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to 'breed' controllersmore » for the task at hand in simulation and the fittest controllers are transferred onto hardware for further validation and testing. ANT facilitates 'machine creativity', with the emergence of novel functionality through a process of self-organized task decomposition of mission goals. ANT based controllers are shown to exhibit self-organization, employ stigmergy (communication mediated through the environment) and make use of templates (unlabeled environmental cues). With lunar in-situ resource utilization (ISRU) efforts in mind, ANT controllers have been tested on a multirobot excavation task in which teams of robots with no explicit supervision can successfully avoid obstacles, interpret excavation blueprints, perform layered digging, avoid burying or trapping other robots and clear/maintain digging routes.« less

Biomimetic molecular design tools that learn, evolve, and adapt

PubMed Central

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

Glucose Synthesis in a Protein-Based Artificial Photosynthesis System.

PubMed

Lu, Hao; Yuan, Wenqiao; Zhou, Jack; Chong, Parkson Lee-Gau

2015-09-01

The objective of this study was to understand glucose synthesis of a protein-based artificial photosynthesis system affected by operating conditions, including the concentrations of reactants, reaction temperature, and illumination. Results from non-vesicle-based glyceraldehyde-3-phosphate (GAP) and glucose synthesis showed that the initial concentrations of ribulose-1,5-bisphosphate (RuBP) and adenosine triphosphate (ATP), lighting source, and temperature significantly affected glucose synthesis. Higher initial concentrations of RuBP and ATP significantly enhanced GAP synthesis, which was linearly correlated to glucose synthesis, confirming the proper functions of all catalyzing enzymes in the system. White fluorescent light inhibited artificial photosynthesis and reduced glucose synthesis by 79.2 % compared to in the dark. The reaction temperature of 40 °C was optimum, whereas lower or higher temperature reduced glucose synthesis. Glucose synthesis in the vesicle-based artificial photosynthesis system reconstituted with bacteriorhodopsin, F 0 F 1 ATP synthase, and polydimethylsiloxane-methyloxazoline-polydimethylsiloxane triblock copolymer was successfully demonstrated. This system efficiently utilized light-induced ATP to drive glucose synthesis, and 5.2 μg ml(-1) glucose was synthesized in 0.78-ml reaction buffer in 7 h. Light-dependent reactions were found to be the bottleneck of the studied artificial photosynthesis system.

Artificial Intelligence and Expert Systems.

ERIC Educational Resources Information Center

Wilson, Harold O.; Burford, Anna Marie

1990-01-01

Delineates artificial intelligence/expert systems (AI/ES) concepts; provides an exposition of some business application areas; relates progress; and creates an awareness of the benefits, limitations, and reservations of AI/ES. (Author)

The 'Biologically-Inspired Computing' Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2006-01-01

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Revealing evolutionary pathways by fitness landscape reconstruction.

PubMed

Kogenaru, Manjunatha; de Vos, Marjon G J; Tans, Sander J

2009-01-01

The concept of epistasis has since long been used to denote non-additive fitness effects of genetic changes and has played a central role in understanding the evolution of biological systems. Owing to an array of novel experimental methodologies, it has become possible to experimentally determine epistatic interactions as well as more elaborate genotype-fitness maps. These data have opened up the investigation of a host of long-standing questions in evolutionary biology, such as the ruggedness of fitness landscapes and the accessibility of mutational trajectories, the evolution of sex, and the origin of robustness and modularity. Here we review this recent and timely marriage between systems biology and evolutionary biology, which holds the promise to understand evolutionary dynamics in a more mechanistic and predictive manner.

Achieving sustainable plant disease management through evolutionary principles.

PubMed

Zhan, Jiasui; Thrall, Peter H; Burdon, Jeremy J

2014-09-01

Plants and their pathogens are engaged in continuous evolutionary battles and sustainable disease management requires novel systems to create environments conducive for short-term and long-term disease control. In this opinion article, we argue that knowledge of the fundamental factors that drive host-pathogen coevolution in wild systems can provide new insights into disease development in agriculture. Such evolutionary principles can be used to guide the formulation of sustainable disease management strategies which can minimize disease epidemics while simultaneously reducing pressure on pathogens to evolve increased infectivity and aggressiveness. To ensure agricultural sustainability, disease management programs that reflect the dynamism of pathogen population structure are essential and evolutionary biologists should play an increasing role in their design. Copyright © 2014 Elsevier Ltd. All rights reserved.

Artificial Intelligence and Expert Systems Research and Their Possible Impact on Information Science.

ERIC Educational Resources Information Center

Borko, Harold

1985-01-01

Defines artificial intelligence (AI) and expert systems; describes library applications utilizing AI to automate creation of document representations, request formulations, and design and modify search strategies for information retrieval systems; discusses expert system development for information services; and reviews impact of these…

Human-technology Integration

NASA Astrophysics Data System (ADS)

Mullen, Katharine M.

Human-technology integration is the replacement of human parts and extension of human capabilities with engineered devices and substrates. Its result is hybrid biological-artificial systems. We discuss here four categories of products furthering human-technology integration: wearable computers, pervasive computing environments, engineered tissues and organs, and prosthetics, and introduce examples of currently realized systems in each category. We then note that realization of a completely artificial sytem via the path of human-technology integration presents the prospect of empirical confirmation of an aware artificially embodied system.

NASA JSC neural network survey results

NASA Technical Reports Server (NTRS)

Greenwood, Dan

1987-01-01

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc.

Artificial-intelligence-based optimization of the management of snow removal assets and resources.

DOT National Transportation Integrated Search

2002-10-01

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent : snow removal asset management system (SRAMS). The system has been evaluated through a case study examining : snow removal from the ...

ROSIE: A Programming Environment for Expert Systems

DTIC Science & Technology

1985-10-01

ence on Artificial Inteligence , Tbilisi, USSR, 1975. Fain, J., D. Gorlin, F. Hayes-Roth, S. Rosenschein, H. Sowizral, and D. Waterman, The ROSIE Language...gramming environment for artificial intelligence (AI) applications. It provides particular support for designing expert systems, systems that embody

A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments

NASA Technical Reports Server (NTRS)

Hancock, Thomas M., III

1994-01-01

This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.

Model-Based Reasoning in the Detection of Satellite Anomalies

DTIC Science & Technology

1990-12-01

Conference on Artificial Intellegence . 1363-1368. Detroit, Michigan, August 89. Chu, Wei-Hai. "Generic Expert System Shell for Diagnostic Reasoning... Intellegence . 1324-1330. Detroit, Michigan, August 89. de Kleer, Johan and Brian C. Williams. "Diagnosing Multiple Faults," Artificial Intellegence , 32(1): 97...Benjamin Kuipers. "Model-Based Monitoring of Dynamic Systems," Proceedings of the Eleventh Intematianal Joint Conference on Artificial Intellegence . 1238

A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

NASA Astrophysics Data System (ADS)

Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain

2016-03-01

Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

Paradox in AI - AI 2.0: The Way to Machine Consciousness

NASA Astrophysics Data System (ADS)

Palensky, Peter; Bruckner, Dietmar; Tmej, Anna; Deutsch, Tobias

Artificial Intelligence, the big promise of the last millennium, has apparently made its way into our daily lives. Cell phones with speech control, evolutionary computing in data mining or power grids, optimized via neural network, show its applicability in industrial environments. The original expectation of true intelligence and thinking machines lies still ahead of us. Researchers are, however, optimistic as never before. This paper tries to compare the views, challenges and approaches of several disciplines: engineering, psychology, neuroscience, philosophy. It gives a short introduction to Psychoanalysis, discusses the term consciousness, social implications of intelligent machines, related theories, and expectations and shall serve as a starting point for first attempts of combining these diverse thoughts.

An evolutionary critique of cultural analysis in sociology.

PubMed

Crippen, T

1992-12-01

A noteworthy development that has transpired in American sociology in the past quarter century has been the increasingly sophisticated interest in the analysis of human cultural systems. Sadly, however, these analyses reveal that social scientists rarely appreciate the profoundly evolutionary aspects of human culture. The chief purpose of this essay is to address this shortcoming and to offer some tentative suggestions toward its rectification. The essay begins by briefly reviewing recent developments in the analysis of cultural systems, primarily by reference to the influential work of Wuthnow. Second, a common flaw in these approaches is addressed-namely, the absence of any recognition of the value of grounding sociocultural theory in an informed evolutionary framework-and the case is made that this shortcoming is avoidable, even within the context of the intellectual traditions of the social sciences. Third, the evolutionary foundations of human cultural behavior are explored in terms of an analysis of relevant theoretical and empirical developments in the evolutionary neurosciences. Fourth, the value of these insights is illustrated by reference to an evolutionary critique of a recent and thought-provoking contribution to the study of modern political culture-Douglas and Wildavsky's analysis ofRisk and Culture. Finally, the article concludes by emphasizing the value of and the necessity for incorporating evolutionary reasoning into the domain of sociocultural theory.

Application of evolutionary computation in ECAD problems

NASA Astrophysics Data System (ADS)

Lee, Dae-Hyun; Hwang, Seung H.

1998-10-01

Design of modern electronic system is a complicated task which demands the use of computer- aided design (CAD) tools. Since a lot of problems in ECAD are combinatorial optimization problems, evolutionary computations such as genetic algorithms and evolutionary programming have been widely employed to solve those problems. We have applied evolutionary computation techniques to solve ECAD problems such as technology mapping, microcode-bit optimization, data path ordering and peak power estimation, where their benefits are well observed. This paper presents experiences and discusses issues in those applications.

Fatal Attraction of Short-Tailed Shearwaters to Artificial Lights

PubMed Central

Rodríguez, Airam; Burgan, Graeme; Dann, Peter; Jessop, Roz; Negro, Juan J.; Chiaradia, Andre

2014-01-01

Light pollution is increasing around the world and altering natural nightscapes with potential ecological and evolutionary consequences. A severe ecological perturbation caused by artificial lights is mass mortalities of organisms, including seabird fledglings that are attracted to lights at night on their first flights to the sea. Here, we report on the number of fledging short-tailed shearwaters Ardenna tenuirostris found grounded in evening and morning rescue patrols conducted at Phillip Island, Australia, during a 15-year period (1999–2013). We assessed factors affecting numbers of grounded birds and mortality including date, moon phase, wind direction and speed, number of visitors and holiday periods. We also tested experimentally if birds were attracted to lights by turning the lights off on a section of the road. Of 8871 fledglings found, 39% were dead or dying. This mortality rate was 4–8 times higher than reported elsewhere for other shearwater species, probably because searching for fledglings was part of our systematic rescue effort rather than the opportunistic rescue used elsewhere. Thus, it suggests that light-induced mortality of seabirds is usually underestimated. We rescued more birds (dead and alive) in peak fledging, moonless and windy nights. Mortality increased through the fledging period, in the mornings and with increased traffic on holiday periods. Turning the road lights off decreased the number of grounded birds (dead and alive). While moon, wind and time are uncontrolled natural constraints, we demonstrated that reduction of light pollution and better traffic management can mitigate artificial light-induced mortality. PMID:25334014

Germ cell regeneration-mediated, enhanced mutagenesis in the ascidian Ciona intestinalis reveals flexible germ cell formation from different somatic cells.

PubMed

Yoshida, Keita; Hozumi, Akiko; Treen, Nicholas; Sakuma, Tetsushi; Yamamoto, Takashi; Shirae-Kurabayashi, Maki; Sasakura, Yasunori

2017-03-15

The ascidian Ciona intestinalis has a high regeneration capacity that enables the regeneration of artificially removed primordial germ cells (PGCs) from somatic cells. We utilized PGC regeneration to establish efficient methods of germ line mutagenesis with transcription activator-like effector nucleases (TALENs). When PGCs were artificially removed from animals in which a TALEN pair was expressed, somatic cells harboring mutations in the target gene were converted into germ cells, this germ cell population exhibited higher mutation rates than animals not subjected to PGC removal. PGC regeneration enables us to use TALEN expression vectors of specific somatic tissues for germ cell mutagenesis. Unexpectedly, cis elements for epidermis, neural tissue and muscle could be used for germ cell mutagenesis, indicating there are multiple sources of regenerated PGCs, suggesting a flexibility of differentiated Ciona somatic cells to regain totipotency. Sperm and eggs of a single hermaphroditic, PGC regenerated animal typically have different mutations, suggesting they arise from different cells. PGCs can be generated from somatic cells even though the maternal PGCs are not removed, suggesting that the PGC regeneration is not solely an artificial event but could have an endogenous function in Ciona. This study provides a technical innovation in the genome-editing methods, including easy establishment of mutant lines. Moreover, this study suggests cellular mechanisms and the potential evolutionary significance of PGC regeneration in Ciona. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization

ERIC Educational Resources Information Center

Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.

2013-01-01

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic…

NASA's Evolutionary Xenon Thruster (NEXT) Ion Propulsion System Information Summary

NASA Technical Reports Server (NTRS)

Pencil, Eirc S.; Benson, Scott W.

2008-01-01

This document is a guide to New Frontiers mission proposal teams. The document describes the development and status of the NASA's Evolutionary Xenon Thruster (NEXT) ion propulsion system (IPS) technology, its application to planetary missions, and the process anticipated to transition NEXT to the first flight mission.

Manager’s Guide to Technology Transition in an Evolutionary Acquisition Environment

DTIC Science & Technology

2005-06-01

program managers, product managers, staffs, and organizations that manage the development , procurement, production, and fielding of systems...rapidly advancing technologies. Technology transitions can occur during the development of systems, or even after a system has been in the field ...Documentation Evolutionary acquisition is an acquisition strategy that defines, develops , produces or acquires, and fields an initial hardware or software

Counseling, Artificial Intelligence, and Expert Systems.

ERIC Educational Resources Information Center

Illovsky, Michael E.

1994-01-01

Considers the use of artificial intelligence and expert systems in counseling. Limitations are explored; candidates for counseling versus those for expert systems are discussed; programming considerations are reviewed; and techniques for dealing with rational, nonrational, and irrational thoughts and feelings are described. (Contains 46…

Artificial Intelligence and Vocational Education: An Impending Confluence.

ERIC Educational Resources Information Center

Roth, Gene L.; McEwing, Richard A.

1986-01-01

Reports on the relatively new field of artificial intelligence and its relationship to vocational education. Compares human intelligence with artificial intelligence. Discusses expert systems, natural language technology, and current trends. Lists potential applications for vocational education. (CH)

Evolutionary game based control for biological systems with applications in drug delivery.

PubMed

Li, Xiaobo; Lenaghan, Scott C; Zhang, Mingjun

2013-06-07

Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. Copyright © 2013 Elsevier Ltd. All rights reserved.

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

PubMed

Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

2014-10-01

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

Production traits of artificially and naturally hatched geese in intensive and free-range systems - II: slaughter, carcass and meat quality traits.

PubMed

Boz, M A; Sarıca, M; Yamak, U S

2017-04-01

1. This study investigates the slaughter, carcass and meat quality traits of artificially and naturally hatched geese in intensive and free-range production systems. 2. The study was conducted with 114 naturally hatched and 102 artificially hatched geese. From each replicate of the intensive and free-range systems, one female and one male goose were slaughtered at the ages of 14, 16 and 18 weeks (a total of 32 geese per slaughter week). 3. Artificially hatched geese had higher slaughter weights (5280 vs. 4404 g), carcass weights (3520 vs. 2863), dressing percentages (66.6-65.2% vs. 65.0-63.6%) and carcass part, feather and edible inner organ weights. The ratio of both edible inner organs and abdominal fat was higher in naturally hatched geese. Breast meat L*, a* and pH values and thigh meat dry matter values were higher in artificially hatched geese, whereas thigh meat b* and pH values were higher in naturally hatched geese. 4. Intensively reared geese had higher slaughter weights (4900 vs. 4783 g), carcass weights (3253 vs. 3130 g) and abdominal fat weights (280 vs. 250 g), as well as higher dressing percentages (66.3-64.9% vs. 65.3-63.9%). Breast meat b* and thigh meat L* values were higher in the intensive system, while breast and thigh pH values, dripping loss and cooking loss were higher in the free-range system. Water-holding capacity was higher in the intensive system. 5. In conclusion, artificially hatched, intensively reared geese had the highest slaughter weights; however, both artificially and naturally hatched geese raised in a free-range system reached acceptable slaughter weights and can thus be recommended for use with this type of production system.

Bibliography: Artificial Intelligence.

ERIC Educational Resources Information Center

Smith, Richard L.

1986-01-01

Annotates reference material on artificial intelligence, mostly at an introductory level, with applications to education and learning. Topics include: (1) programing languages; (2) expert systems; (3) language instruction; (4) tutoring systems; and (5) problem solving and reasoning. (JM)

Distant touch hydrodynamic imaging with an artificial lateral line.

PubMed

Yang, Yingchen; Chen, Jack; Engel, Jonathan; Pandya, Saunvit; Chen, Nannan; Tucker, Craig; Coombs, Sheryl; Jones, Douglas L; Liu, Chang

2006-12-12

Nearly all underwater vehicles and surface ships today use sonar and vision for imaging and navigation. However, sonar and vision systems face various limitations, e.g., sonar blind zones, dark or murky environments, etc. Evolved over millions of years, fish use the lateral line, a distributed linear array of flow sensing organs, for underwater hydrodynamic imaging and information extraction. We demonstrate here a proof-of-concept artificial lateral line system. It enables a distant touch hydrodynamic imaging capability to critically augment sonar and vision systems. We show that the artificial lateral line can successfully perform dipole source localization and hydrodynamic wake detection. The development of the artificial lateral line is aimed at fundamentally enhancing human ability to detect, navigate, and survive in the underwater environment.

Optimization of an artificial-recharge-pumping system for water supply in the Maghaway Valley, Cebu, Philippines

NASA Astrophysics Data System (ADS)

Kawo, Nafyad Serre; Zhou, Yangxiao; Magalso, Ronnell; Salvacion, Lasaro

2018-05-01

A coupled simulation-optimization approach to optimize an artificial-recharge-pumping system for the water supply in the Maghaway Valley, Cebu, Philippines, is presented. The objective is to maximize the total pumping rate through a system of artificial recharge and pumping while meeting constraints such as groundwater-level drawdown and bounds on pumping rates at each well. The simulation models were coupled with groundwater management optimization to maximize production rates. Under steady-state natural conditions, the significant inflow to the aquifer comes from river leakage, whereas the natural discharge is mainly the subsurface outflow to the downstream area. Results from the steady artificial-recharge-pumping simulation model show that artificial recharge is about 20,587 m3/day and accounts for 77% of total inflow. Under transient artificial-recharge-pumping conditions, artificial recharge varies between 14,000 and 20,000 m3/day depending on the wet and dry seasons, respectively. The steady-state optimisation results show that the total optimal abstraction rate is 37,545 m3/day and artificial recharge is increased to 29,313 m3/day. The transient optimization results show that the average total optimal pumping rate is 36,969 m3/day for the current weir height. The transient optimization results for an increase in weir height by 1 and 2 m show that the average total optimal pumping rates are increased to 38,768 and 40,463 m3/day, respectively. It is concluded that the increase in the height of the weir can significantly increase the artificial recharge rate and production rate in Maghaway Valley.

List of ARI Conference Papers, Journal Articles, Books, and Book Chapters: 1982-1991

DTIC Science & Technology

1992-10-01

and Engineering Applications of Artificial Intelligence and Expert Systems, Tullahoma, TN. Goehring, D.J., & Hart, R.J. (1985, October). Automated...systems: Computkr-based authoring. Proceedings of the 30th annual meeting of the Artificial Intelligence Society, Dayton, OH. Knapp, D.J., & Pliske, R.M...Moses, F.L. (1984-85) Intelligence vehicle integrated displays. Paper presented at the Conference on Applied Artificial Intelligence , the Data Processing

Actors: A Model of Concurrent Computation in Distributed Systems.

DTIC Science & Technology

1985-06-01

Artificial Intelligence Labora- tory of the Massachusetts Institute of Technology. Support for the labora- tory’s aritificial intelligence research is...RD-A157 917 ACTORS: A MODEL OF CONCURRENT COMPUTATION IN 1/3- DISTRIBUTED SY𔃿TEMS(U) MASSACHUSETTS INST OF TECH CRMBRIDGE ARTIFICIAL INTELLIGENCE ...Computation In Distributed Systems Gui A. Aghai MIT Artificial Intelligence Laboratory Thsdocument ha. been cipp-oved I= pblicrelease and sale; itsI

Artificial “ping-pong” cascade of PIWI-interacting RNA in silkworm cells

PubMed Central

Shoji, Keisuke; Suzuki, Yutaka; Sugano, Sumio; Shimada, Toru; Katsuma, Susumu

2017-01-01

PIWI-interacting RNAs (piRNAs) play essential roles in the defense system against selfish elements in animal germline cells by cooperating with PIWI proteins. A subset of piRNAs is predicted to be generated via the “ping-pong” cascade, which is mainly controlled by two different PIWI proteins. Here we established a cell-based artificial piRNA production system using a silkworm ovarian cultured cell line that is believed to possess a complete piRNA pathway. In addition, we took advantage of a unique silkworm sex-determining one-to-one ping-pong piRNA pair, which enabled us to precisely monitor the behavior of individual artificial piRNAs. With this novel strategy, we successfully generated artificial piRNAs against endogenous protein-coding genes via the expected back-and-forth traveling mechanism. Furthermore, we detected “primary” piRNAs from the upstream region of the artificial “ping-pong” site in the endogenous gene. This artificial piRNA production system experimentally confirms the existence of the “ping-pong” cascade of piRNAs. Also, this system will enable us to identify the factors involved in both, or each, of the “ping” and “pong” cascades and the sequence features that are required for efficient piRNA production. PMID:27777367

Three-Dimensional Effects of Artificial Mixing in a Shallow Drinking-Water Reservoir

NASA Astrophysics Data System (ADS)

Chen, Shengyang; Little, John C.; Carey, Cayelan C.; McClure, Ryan P.; Lofton, Mary E.; Lei, Chengwang

2018-01-01

Studies that examine the effects of artificial mixing for water-quality mitigation in lakes and reservoirs often view a water column with a one-dimensional (1-D) perspective (e.g., homogenized epilimnetic and hypolimnetic layers). Artificial mixing in natural water bodies, however, is inherently three dimensional (3-D). Using a 3-D approach experimentally and numerically, the present study visualizes thermal structure and analyzes constituent transport under the influence of artificial mixing in a shallow drinking-water reservoir. The purpose is to improve the understanding of artificial mixing, which may help to better design and operate mixing systems. In this reservoir, a side-stream supersaturation (SSS) hypolimnetic oxygenation system and an epilimnetic bubble-plume mixing (EM) system were concurrently deployed in the deep region. The present study found that, while the mixing induced by the SSS system does not have a distinct 3-D effect on the thermal structure, epilimnetic mixing by the EM system causes 3-D heterogeneity. In the experiments, epilimnetic mixing deepened the lower metalimnetic boundary near the diffuser by about 1 m, with 55% reduction of the deepening rate at 120 m upstream of the diffuser. In a tracer study using a 3-D hydrodynamic model, the operational flow rate of the EM system is found to be an important short-term driver of constituent transport in the reservoir, whereas the duration of the EM system operation is the dominant long-term driver. The results suggest that artificial mixing substantially alters both 3-D thermal structure and constituent transport, and thus needs to be taken into account for reservoir management.

Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

2003-01-01

Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

Evolution of tag-based cooperation on Erdős-Rényi random graphs

NASA Astrophysics Data System (ADS)

Lima, F. W. S.; Hadzibeganovic, Tarik; Stauffer, Dietrich

2014-12-01

Here, we study an agent-based model of the evolution of tag-mediated cooperation on Erdős-Rényi random graphs. In our model, agents with heritable phenotypic traits play pairwise Prisoner's Dilemma-like games and follow one of the four possible strategies: Ethnocentric, altruistic, egoistic and cosmopolitan. Ethnocentric and cosmopolitan strategies are conditional, i.e. their selection depends upon the shared phenotypic similarity among interacting agents. The remaining two strategies are always unconditional, meaning that egoists always defect while altruists always cooperate. Our simulations revealed that ethnocentrism can win in both early and later evolutionary stages on directed random graphs when reproduction of artificial agents was asexual; however, under the sexual mode of reproduction on a directed random graph, we found that altruists dominate initially for a rather short period of time, whereas ethnocentrics and egoists suppress other strategists and compete for dominance in the intermediate and later evolutionary stages. Among our results, we also find surprisingly regular oscillations which are not damped in the course of time even after half a million Monte Carlo steps. Unlike most previous studies, our findings highlight conditions under which ethnocentrism is less stable or suppressed by other competing strategies.

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

PubMed

Orzechowski, Patryk; Sipper, Moshe; Huang, Xiuzhen; Moore, Jason H

2018-05-22

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. In this paper a novel biclustering algorithm based on evolutionary computation, a subfield of artificial intelligence (AI), is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic. Correspondence and requests for materials should be addressed to P.O. (email: patryk.orzechowski@gmail.com) and J.H.M. (email: jhmoore@upenn.edu). Supplementary Data with results of analyses and additional information on the method is available at Bioinformatics online.

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat.

PubMed

Jeong, Haeyoung; Lee, Sang J; Kim, Pil

2016-09-20

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) refers to the experimental situation in which evolution is observed using living organisms under controlled conditions and stressors; organisms are thereby artificially forced to make evolutionary changes. Microorganisms are subject to a variety of stressors in the environment and are capable of regulating certain stress-inducible proteins to increase their chances of survival. Naturally occurring spontaneous mutations bring about changes in a microorganism's genome that affect its chances of survival. Long-term exposure to chemostat culture provokes an accumulation of spontaneous mutations and renders the most adaptable strain dominant. Compared to the colony transfer and serial transfer methods, chemostat culture entails the highest number of cell divisions and, therefore, the highest number of diverse populations. Although chemostat culture for ALE requires more complicated culture devices, it is less labor intensive once the operation begins. Comparative genomic and transcriptome analyses of the adapted strain provide evolutionary clues as to how the stressors contribute to mutations that overcome the stress. The goal of the current paper is to bring about accelerated evolution of microorganisms under controlled laboratory conditions.

Multi Objective Optimization of Yarn Quality and Fibre Quality Using Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Ghosh, Anindya; Das, Subhasis; Banerjee, Debamalya

2013-03-01

The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a good quality yarn with added cost benefit. The present work does a multi-objective optimization on two objectives, viz. maximization of cotton yarn strength and minimization of raw material quality. The first objective function has been formulated based on the artificial neural network input-output relation between cotton fibre properties and yarn strength. The second objective function is formulated with the well known regression equation of spinning consistency index. It is obvious that these two objectives are conflicting in nature i.e. not a single combination of cotton fibre parameters does exist which produce maximum yarn strength and minimum cotton fibre quality simultaneously. Therefore, it has several optimal solutions from which a trade-off is needed depending upon the requirement of user. In this work, the optimal solutions are obtained with an elitist multi-objective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). These optimum solutions may lead to the efficient exploitation of raw materials to produce better quality yarns at low costs.

Construction of artificial cilia from microtubules and kinesins through a well-designed bottom-up approach.

PubMed

Sasaki, Ren; Kabir, Arif Md Rashedul; Inoue, Daisuke; Anan, Shizuka; Kimura, Atsushi P; Konagaya, Akihiko; Sada, Kazuki; Kakugo, Akira

2018-04-05

Self-organized structures of biomolecular motor systems, such as cilia and flagella, play key roles in the dynamic processes of living organisms, like locomotion or the transportation of materials. Although fabrication of such self-organized structures from reconstructed biomolecular motor systems has attracted much attention in recent years, a systematic construction methodology is still lacking. In this work, through a bottom-up approach, we fabricated artificial cilia from a reconstructed biomolecular motor system, microtubule/kinesin. The artificial cilia exhibited a beating motion upon the consumption, by the kinesins, of the chemical energy obtained from the hydrolysis of adenosine triphosphate (ATP). Several design parameters, such as the length of the microtubules, the density of the kinesins along the microtubules, the depletion force among the microtubules, etc., have been identified, which permit tuning of the beating frequency of the artificial cilia. The beating frequency of the artificial cilia increases upon increasing the length of the microtubules, but declines for the much longer microtubules. A high density of the kinesins along the microtubules is favorable for the beating motion of the cilia. The depletion force induced bundling of the microtubules accelerated the beating motion of the artificial cilia and increased the beating frequency. This work helps understand the role of self-assembled structures of the biomolecular motor systems in the dynamics of living organisms and is expected to expedite the development of artificial nanomachines, in which the biomolecular motors may serve as actuators.

[Succession pattern of artificial vegetation community and its ecological mechanism in an arid desert region].

PubMed

Xu, Cailin; Li, Zizhen

2003-09-01

Focusing on the artificial vegetation protection system of the Shapotou section of Baotou-Lanzhou railway in the arid desert region of China, this paper examined the dynamics of dominant plant species and the succession pattern of artificial plant community in the process of establishing and developing regional artificial vegetation. It also studied the driving force and the ecologically intrinsic mechanism of the community succession. The results demonstrated that the species composition of the artificial vegetation dramatically changed after 40 years of succession, from original artificial plant community of shrub and semi-shrub to artificial-natural desert plant community with annual herb dominated. During the process of succession, the importance values of artificial shrubs, such as Caragana korshinskii and Hedysarum scoparius, decreased and gradually retreated from the artificial plant community, while the naturally multiplied annual herb, such as Eragrostis poaeoides, Bassia dasyphylla, Salsola ruthenica, Chloris virgata and etc., were presented one after another and gradually became dominant. Besides, Artemisia ordosica always played a key role in the community due to its ability of naturally sowing and self-replacement. This type of succession pattern was closely related to the shortage of precipitation resource in this region and the formation of soil crust which inhibited the reproduction of shrub and perennial herb with deep root systems. This study provided a theoretical ground for realizing persistent development of artificial plant community.

Systems metabolic engineering strategies for the production of amino acids.

PubMed

Ma, Qian; Zhang, Quanwei; Xu, Qingyang; Zhang, Chenglin; Li, Yanjun; Fan, Xiaoguang; Xie, Xixian; Chen, Ning

2017-06-01

Systems metabolic engineering is a multidisciplinary area that integrates systems biology, synthetic biology and evolutionary engineering. It is an efficient approach for strain improvement and process optimization, and has been successfully applied in the microbial production of various chemicals including amino acids. In this review, systems metabolic engineering strategies including pathway-focused approaches, systems biology-based approaches, evolutionary approaches and their applications in two major amino acid producing microorganisms: Corynebacterium glutamicum and Escherichia coli, are summarized.

What have humans done for evolutionary biology? Contributions from genes to populations.

PubMed

Briga, Michael; Griffin, Robert M; Berger, Vérane; Pettay, Jenni E; Lummaa, Virpi

2017-11-15

Many fundamental concepts in evolutionary biology were discovered using non-human study systems. Humans are poorly suited to key study designs used to advance this field, and are subject to cultural, technological, and medical influences often considered to restrict the pertinence of human studies to other species and general contexts. Whether studies using current and recent human populations provide insights that have broader biological relevance in evolutionary biology is, therefore, frequently questioned. We first surveyed researchers in evolutionary biology and related fields on their opinions regarding whether studies on contemporary humans can advance evolutionary biology. Almost all 442 participants agreed that humans still evolve, but fewer agreed that this occurs through natural selection. Most agreed that human studies made valuable contributions to evolutionary biology, although those less exposed to human studies expressed more negative views. With a series of examples, we discuss strengths and limitations of evolutionary studies on contemporary humans. These show that human studies provide fundamental insights into evolutionary processes, improve understanding of the biology of many other species, and will make valuable contributions to evolutionary biology in the future. © 2017 The Author(s).

What have humans done for evolutionary biology? Contributions from genes to populations

PubMed Central

Briga, Michael; Griffin, Robert M.; Berger, Vérane; Pettay, Jenni E.

2017-01-01

Many fundamental concepts in evolutionary biology were discovered using non-human study systems. Humans are poorly suited to key study designs used to advance this field, and are subject to cultural, technological, and medical influences often considered to restrict the pertinence of human studies to other species and general contexts. Whether studies using current and recent human populations provide insights that have broader biological relevance in evolutionary biology is, therefore, frequently questioned. We first surveyed researchers in evolutionary biology and related fields on their opinions regarding whether studies on contemporary humans can advance evolutionary biology. Almost all 442 participants agreed that humans still evolve, but fewer agreed that this occurs through natural selection. Most agreed that human studies made valuable contributions to evolutionary biology, although those less exposed to human studies expressed more negative views. With a series of examples, we discuss strengths and limitations of evolutionary studies on contemporary humans. These show that human studies provide fundamental insights into evolutionary processes, improve understanding of the biology of many other species, and will make valuable contributions to evolutionary biology in the future. PMID:29118130

Artificial Virus Delivers CRISPR-Cas9 System for Genome Editing of Cells in Mice.

PubMed

Li, Ling; Song, Linjiang; Liu, Xiaowei; Yang, Xi; Li, Xia; He, Tao; Wang, Ning; Yang, Suleixin; Yu, Chuan; Yin, Tao; Wen, Yanzhu; He, Zhiyao; Wei, Xiawei; Su, Weijun; Wu, Qinjie; Yao, Shaohua; Gong, Changyang; Wei, Yuquan

2017-01-24

CRISPR-Cas9 has emerged as a versatile genome-editing platform. However, due to the large size of the commonly used CRISPR-Cas9 system, its effective delivery has been a challenge and limits its utility for basic research and therapeutic applications. Herein, a multifunctional nucleus-targeting "core-shell" artificial virus (RRPHC) was constructed for the delivery of CRISPR-Cas9 system. The artificial virus could efficiently load with the CRISPR-Cas9 system, accelerate the endosomal escape, and promote the penetration into the nucleus without additional nuclear-localization signal, thus enabling targeted gene disruption. Notably, the artificial virus is more efficient than SuperFect, Lipofectamine 2000, and Lipofectamine 3000. When loaded with a CRISPR-Cas9 plasmid, it induced higher targeted gene disruption efficacy than that of Lipofectamine 3000. Furthermore, the artificial virus effectively targets the ovarian cancer via dual-receptor-mediated endocytosis and had minimum side effects. When loaded with the Cas9-hMTH1 system targeting MTH1 gene, RRPHC showed effective disruption of MTH1 in vivo. This strategy could be adapted for delivering CRISPR-Cas9 plasmid or other functional nucleic acids in vivo.

Carbon dot-Au(i)Ag(0) assembly for the construction of an artificial light harvesting system.

PubMed

Jana, Jayasmita; Aditya, Teresa; Pal, Tarasankar

2018-03-06

Artificial light harvesting systems (LHS) with inorganic counterparts are considered to be robust as well as mechanistically simple, where the system follows the donor-acceptor principle with an unchanged structural pattern. Plasmonic gold or silver nanoparticles are mostly chosen as inorganic counterparts to design artificial LHS. To capitalize on its electron accepting capability, Au(i) has been considered in this work for the synergistic stabilization of a system with intriguingly fluorescing silver(0) clusters produced in situ. Thus a stable fluorescent Au(i)Ag(0) assembly is generated with electron accepting capabilities. On the other hand, carbon dots have evolved as new fluorescent probes due to their unique physicochemical properties. Utilizing the simple electronic behavior of carbon dots, an electronic interaction between the fluorescent Au(i)Ag(0) and a carbon dot has been investigated for the construction of a new artificial light harvesting system. This coinage metal assembly allows surface energy transfer where it acts as an acceptor, while the carbon dot behaves as a good donor. The energy transfer efficiency has been calculated experimentally to be significant (81.3%) and the Au(i)Ag(0)-carbon dot assembly paves the way for efficient artificial LHS.

Evolutionary dynamics with fluctuating population sizes and strong mutualism.

PubMed

Chotibut, Thiparat; Nelson, David R

2015-08-01

Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

Evolutionary dynamics with fluctuating population sizes and strong mutualism

NASA Astrophysics Data System (ADS)

Chotibut, Thiparat; Nelson, David R.

2015-08-01

Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

Evolutionary engineering of industrial microorganisms-strategies and applications.

PubMed

Zhu, Zhengming; Zhang, Juan; Ji, Xiaomei; Fang, Zhen; Wu, Zhimeng; Chen, Jian; Du, Guocheng

2018-06-01

Microbial cells have been widely used in the industry to obtain various biochemical products, and evolutionary engineering is a common method in biological research to improve their traits, such as high environmental tolerance and improvement of product yield. To obtain better integrate functions of microbial cells, evolutionary engineering combined with other biotechnologies have attracted more attention in recent years. Classical laboratory evolution has been proven effective to letting more beneficial mutations occur in different genes but also has some inherent limitations such as a long evolutionary period and uncontrolled mutation frequencies. However, recent studies showed that some new strategies may gradually overcome these limitations. In this review, we summarize the evolutionary strategies commonly used in industrial microorganisms and discuss the combination of evolutionary engineering with other biotechnologies such as systems biology and inverse metabolic engineering. Finally, we prospect the importance and application prospect of evolutionary engineering as a powerful tool especially in optimization of industrial microbial cell factories.

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

The scope and strength of sex-specific selection in genome evolution.

PubMed

Wright, A E; Mank, J E

2013-09-01

Males and females share the vast majority of their genomes and yet are often subject to different, even conflicting, selection. Genomic and transcriptomic developments have made it possible to assess sex-specific selection at the molecular level, and it is clear that sex-specific selection shapes the evolutionary properties of several genomic characteristics, including transcription, post-transcriptional regulation, imprinting, genome structure and gene sequence. Sex-specific selection is strongly influenced by mating system, which also causes neutral evolutionary changes that affect different regions of the genome in different ways. Here, we synthesize theoretical and molecular work in order to provide a cohesive view of the role of sex-specific selection and mating system in genome evolution. We also highlight the need for a combined approach, incorporating both genomic data and experimental phenotypic studies, in order to understand precisely how sex-specific selection drives evolutionary change across the genome. © 2013 The Authors. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

Form of an evolutionary tradeoff affects eco-evolutionary dynamics in a predator-prey system.

PubMed

Kasada, Minoru; Yamamichi, Masato; Yoshida, Takehito

2014-11-11

Evolution on a time scale similar to ecological dynamics has been increasingly recognized for the last three decades. Selection mediated by ecological interactions can change heritable phenotypic variation (i.e., evolution), and evolution of traits, in turn, can affect ecological interactions. Hence, ecological and evolutionary dynamics can be tightly linked and important to predict future dynamics, but our understanding of eco-evolutionary dynamics is still in its infancy and there is a significant gap between theoretical predictions and empirical tests. Empirical studies have demonstrated that the presence of genetic variation can dramatically change ecological dynamics, whereas theoretical studies predict that eco-evolutionary dynamics depend on the details of the genetic variation, such as the form of a tradeoff among genotypes, which can be more important than the presence or absence of the genetic variation. Using a predator-prey (rotifer-algal) experimental system in laboratory microcosms, we studied how different forms of a tradeoff between prey defense and growth affect eco-evolutionary dynamics. Our experimental results show for the first time to our knowledge that different forms of the tradeoff produce remarkably divergent eco-evolutionary dynamics, including near fixation, near extinction, and coexistence of algal genotypes, with quantitatively different population dynamics. A mathematical model, parameterized from completely independent experiments, explains the observed dynamics. The results suggest that knowing the details of heritable trait variation and covariation within a population is essential for understanding how evolution and ecology will interact and what form of eco-evolutionary dynamics will result.

Artificial Intelligence Assists Ultrasonic Inspection

NASA Technical Reports Server (NTRS)

Schaefer, Lloyd A.; Willenberg, James D.

1992-01-01

Subtle indications of flaws extracted from ultrasonic waveforms. Ultrasonic-inspection system uses artificial intelligence to help in identification of hidden flaws in electron-beam-welded castings. System involves application of flaw-classification logic to analysis of ultrasonic waveforms.

Developmental changes in haemocyte morphology in response to Staphylococcus aureus and latex beads in the beetle Tenebrio molitor L.

PubMed

Urbański, Arkadiusz; Adamski, Zbigniew; Rosiński, Grzegorz

2018-01-01

The evolutionary success of insects is undoubtedly related to a well-functioning immune system. This is especially apparent during insect development by the adaptation of individuals to the changing risk of infection. In addition, current studies show that the insect immune system is characterized by some specificity in response to natural pathogens (for example, bacteria, viruses or fungi) and artificial challengers (for example, latex beads or nylon filaments). However, developmental changes and the specificity of immune system reactions simultaneously have not been analysed. Thus, the aim of the present research was to determine changes in haemocyte morphology in response to attenuated Staphylococcus aureus and latex beads across each developmental stage of the beetle Tenebrio molitor. The results of the present research clearly showed differences in the morphology of T. molitor haemocytes during development. The haemocytes of larvae and 4-day-old adult males were characterized by the highest adhesion ability, which was expressed as the largest average surface area, filopodia length and number of filopodia. In contrast, the haemocytes of pupae and 30-day-old adult males had a significantly lower value for these morphological parameters, which was probably related to metamorphosis (pupae) and immunosenescence (30-day-old adults). The haemocytes of the tested individuals reacted differently to the presence of S. aureus and latex beads. The presence of S. aureus led to a significant decrease in all previously mentioned morphological parameters in larvae and in both groups of adult individuals. In these groups, incubation of haemocytes with latex beads caused only a slight decrease in surface area and filopodia length and number. This morphological response of haemocytes to biotic and artificial challengers might be related to an increase in the migration abilities of haemocytes during infection. However, the differences in haemocyte reactivity towards S. aureus and latex beads might be explained by differences in pathogen recognition. Conversely, increased adhesive abilities of pupal haemocytes were also observed, which might be related to the specificity of metamorphosis and the hormonal titre during this developmental stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artificial Intelligence in Astronomy

NASA Astrophysics Data System (ADS)

Devinney, E. J.; Prša, A.; Guinan, E. F.; Degeorge, M.

2010-12-01

From the perspective (and bias) as Eclipsing Binary researchers, we give a brief overview of the development of Artificial Intelligence (AI) applications, describe major application areas of AI in astronomy, and illustrate the power of an AI approach in an application developed under the EBAI (Eclipsing Binaries via Artificial Intelligence) project, which employs Artificial Neural Network technology for estimating light curve solution parameters of eclipsing binary systems.

The role of artificial intelligence and expert systems in increasing STS operations productivity

NASA Technical Reports Server (NTRS)

Culbert, C.

1985-01-01

Artificial Intelligence (AI) is discussed. A number of the computer technologies pioneered in the AI world can make significant contributions to increasing STS operations productivity. Application of expert systems, natural language, speech recognition, and other key technologies can reduce manpower while raising productivity. Many aspects of STS support lend themselves to this type of automation. The artificial intelligence section of the mission planning and analysis division has developed a number of functioning prototype systems which demonstrate the potential gains of applying AI technology.

Expert Systems and Special Education.

ERIC Educational Resources Information Center

Hofmeister, Alan M.; Ferrara, Joseph M.

The application of artificial intelligence to the problems of education is examined. One of the most promising areas in artificial intelligence is expert systems technology which engages the user in a problem-solving diaglogue. Some of the characteristics that make expert systems "intelligent" are identified and exemplified. The rise of…

Computer graphics testbed to simulate and test vision systems for space applications

NASA Technical Reports Server (NTRS)

Cheatham, John B.

1991-01-01

Research activity has shifted from computer graphics and vision systems to the broader scope of applying concepts of artificial intelligence to robotics. Specifically, the research is directed toward developing Artificial Neural Networks, Expert Systems, and Laser Imaging Techniques for Autonomous Space Robots.

Artificial Intelligence: Applications in Education.

ERIC Educational Resources Information Center

Thorkildsen, Ron J.; And Others

1986-01-01

Artificial intelligence techniques are used in computer programs to search out rapidly and retrieve information from very large databases. Programing advances have also led to the development of systems that provide expert consultation (expert systems). These systems, as applied to education, are the primary emphasis of this article. (LMO)

Development of the CODER System: A Testbed for Artificial Intelligence Methods in Information Retrieval.

ERIC Educational Resources Information Center

Fox, Edward A.

1987-01-01

Discusses the CODER system, which was developed to investigate the application of artificial intelligence methods to increase the effectiveness of information retrieval systems, particularly those involving heterogeneous documents. Highlights include the use of PROLOG programing, blackboard-based designs, knowledge engineering, lexicological…

On evolutionary systems.

PubMed

Alvarez de Lorenzana, J M; Ward, L M

1987-01-01

This paper develops a metatheoretical framework for understanding evolutionary systems (systems that develop in ways that increase their own variety). The framework addresses shortcomings seen in other popular systems theories. It concerns both living and nonliving systems, and proposes a metahierarchy of hierarchical systems. Thus, it potentially addresses systems at all descriptive levels. We restrict our definition of system to that of a core system whose parts have a different ontological status than the system, and characterize the core system in terms of five global properties: minimal length interval, minimal time interval, system cycle, total receptive capacity, and system potential. We propose two principles through the interaction of which evolutionary systems develop. The Principle of Combinatorial Expansion describes how a core system realizes its developmental potential through a process of progressive differentiation of the single primal state up to a limit stage. The Principle of Generative Condensation describes how the components of the last stage of combinatorial expansion condense and become the environment for and components of new, enriched systems. The early evolution of the Universe after the "big bang" is discussed in light of these ideas as an example of the application of the framework.

Artificial Intelligence.

ERIC Educational Resources Information Center

Thornburg, David D.

1986-01-01

Overview of the artificial intelligence (AI) field provides a definition; discusses past research and areas of future research; describes the design, functions, and capabilities of expert systems and the "Turing Test" for machine intelligence; and lists additional sources for information on artificial intelligence. Languages of AI are…

Effective Technology Insertion: The Key to Evolutionary Acquisition Program

DTIC Science & Technology

2004-05-03

Army War College, 7 April 2003. 47Orazia A. Di Marca ; Rejto, Stephen B. Rejto and Thomas Gomez, “ Open System Design and Evolutionary Acquisition...to Military Applications-Report No. D-2002-107. 14 June 2002. Di Marca , Orazia A.; Rejto, StephenB., and Gomez, Thomas, “ Open System Design and

Generative Representations for Evolving Families of Designs

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2003-01-01

Since typical evolutionary design systems encode only a single artifact with each individual, each time the objective changes a new set of individuals must be evolved. When this objective varies in a way that can be parameterized, a more general method is to use a representation in which a single individual encodes an entire class of artifacts. In addition to saving time by preventing the need for multiple evolutionary runs, the evolution of parameter-controlled designs can create families of artifacts with the same style and a reuse of parts between members of the family. In this paper an evolutionary design system is described which uses a generative representation to encode families of designs. Because a generative representation is an algorithmic encoding of a design, its input parameters are a way to control aspects of the design it generates. By evaluating individuals multiple times with different input parameters the evolutionary design system creates individuals in which the input parameter controls specific aspects of a design. This system is demonstrated on two design substrates: neural-networks which solve the 3/5/7-parity problem and three-dimensional tables of varying heights.

The Evolution of Human Handedness

PubMed Central

Smaers, Jeroen B; Steele, James; Case, Charleen R; Amunts, Katrin

2013-01-01

There is extensive evidence for an early vertebrate origin of lateralized motor behavior and of related asymmetries in underlying brain systems. We investigate human lateralized motor functioning in a broad comparative context of evolutionary neural reorganization. We quantify evolutionary trends in the fronto-cerebellar system (involved in motor learning) across 46 million years of divergent primate evolution by comparing rates of evolution of prefrontal cortex, frontal motor cortex, and posterior cerebellar hemispheres along individual branches of the primate tree of life. We provide a detailed evolutionary model of the neuroanatomical changes leading to modern human lateralized motor functioning, demonstrating an increased role for the fronto-cerebellar system in the apes dating to their evolutionary divergence from the monkeys (∼30 million years ago (Mya)), and a subsequent shift toward an increased role for prefrontal cortex over frontal motor cortex in the fronto-cerebellar system in the Homo-Pan ancestral lineage (∼10 Mya) and in the human ancestral lineage (∼6 Mya). We discuss these results in the context of cortico-cerebellar functions and their likely role in the evolution of human tool use and speech. PMID:23647442

Evolutionary Conflict Between Maternal and Paternal Interests: Integration with Evolutionary Endocrinology.

PubMed

Mokkonen, Mikael; Koskela, Esa; Mappes, Tapio; Mills, Suzanne C

2016-08-01

Conflict between mates, as well as conflict between parents and offspring are due to divergent evolutionary interests of the interacting individuals. Hormone systems provide genetically based proximate mechanisms for mediating phenotypic adaptation and maladaptation characteristic of evolutionary conflict between individuals. Testosterone (T) is among the most commonly studied hormones in evolutionary biology, and as such, its role in shaping sexually dimorphic behaviors and physiology is relatively well understood, but its role in evolutionary conflict is not as clear. In this review, we outline the genomic conflicts arising within the family unit, and incorporate multiple lines of evidence from the bank vole (Myodes glareolus) system to outline how T impacts traits associated with reproduction and survival, resulting in a sexually antagonistic genetic trade-off in fitness. A major prediction arising from this work is that lower T is favored in females, whereas the optimal T level in males fluctuates in relation to social and ecological factors. We additionally discuss future directions to further integrate endocrinology into the study of sexual and parent-offspring conflicts. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

On the origins of anticipation as an evolutionary framework: functional systems perspective

NASA Astrophysics Data System (ADS)

Kurismaa, Andres

2015-08-01

This paper discusses the problem of anticipation from an evolutionary and systems-theoretical perspective, developed in the context of Russian/Soviet evolutionary biological and neurophysiological schools in the early and mid-twentieth century. On this background, an outline is given of the epigenetic interpretation of anticipatory capacities formulated and substantiated by the eminent Russian neurophysiologist academician Peter K. Anokhin in the framework of functional systems theory. It is considered that several key positions of this theory are well confirmed by recent evidence on anticipation as an evolutionarily basic adaptive capacity, possibly inherent to the organization of life. In the field of neuroscience, the theory of functional systems may potentially facilitate future studies at the intersection of learning, development and evolution by representing an integrative approach to the problem of anticipation.

Artificial eye for in vitro experiments of laser light interaction with aqueous media

NASA Astrophysics Data System (ADS)

Cain, Clarence P.; Noojin, Gary D.; Hammer, Daniel X.; Thomas, Robert J.; Rockwell, Benjamin A.

1997-01-01

An artificial eye has been designed and assembled that mimics the focusing geometry of the living eye. The artificial eye's focusing characteristics are measured and compared with those of the in vivo system. The artificial eye is used to measure several nonlinear optical phenomena that may have an impact on the laser damage thresholds of the retina produced by ultrashort laser pulses. We chose a focal length of 17 mm to simulate the rhesus monkey eye, with a visual cone angle of 8.4 deg for a 2.5-mm diameter laser beam input. The measured focal point image diameter was 5.6 plus or minus 1 micrometer, which was 1.5 times the calculated diffraction-limited image diameter. This focusing system had the best M2 of all the systems evaluated. We used the artificial eye to measure the threshold for laser- induced breakdown, stimulated Brillouin scattering, super- continuum generation, and pulse temporal broadening due to group velocity dispersion.

Exploiting Lexical Regularities in Designing Natural Language Systems.

DTIC Science & Technology

1988-04-01

ELEMENT. PROJECT. TASKN Artificial Inteligence Laboratory A1A4WR NTumet 0) 545 Technology Square Cambridge, MA 02139 Ln *t- CONTROLLING OFFICE NAME AND...RO-RI95 922 EXPLOITING LEXICAL REGULARITIES IN DESIGNING NATURAL 1/1 LANGUAGE SYSTENS(U) MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE...oes.ary and ftdou.Ip hr Nl wow" L,2This paper presents the lexical component of the START Question Answering system developed at the MIT Artificial

Current state of total artificial heart therapy and introduction of the most important total artificial heart systems.

PubMed

Spiliopoulos, Sotirios; Hergesell, Vera; Wasler, Andrae; Dapunt, Otto

2018-06-14

Due to the declining instances of organ donation, total artificial heart (TAH) therapy is of increasing importance for the management of end-stage biventricular heart failure. We introduce the currently most important established and novel TAH systems (SynCardia, CARMAT, ReinHeart, BiVACOR), report clinical outcomes and discuss technical requirements for the successful implementation of TAH therapy as an alternative to cardiac transplantation.

Computer graphics testbed to simulate and test vision systems for space applications

NASA Technical Reports Server (NTRS)

Cheatham, John B.

1991-01-01

Artificial intelligence concepts are applied to robotics. Artificial neural networks, expert systems and laser imaging techniques for autonomous space robots are being studied. A computer graphics laser range finder simulator developed by Wu has been used by Weiland and Norwood to study use of artificial neural networks for path planning and obstacle avoidance. Interest is expressed in applications of CLIPS, NETS, and Fuzzy Control. These applications are applied to robot navigation.

Intraspecific eye color variability in birds and mammals: a recent evolutionary event exclusive to humans and domestic animals.

PubMed

Negro, Juan J; Carmen Blázquez, M; Galván, Ismael

2017-01-01

Human populations and breeds of domestic animals are composed of individuals with a multiplicity of eye (= iris) colorations. Some wild birds and mammals may have intraspecific eye color variability, but this variation seems to be due to the developmental stage of the individual, its breeding status, and/or sexual dimorphism. In other words, eye colour tends to be a species-specific trait in wild animals, and the exceptions are species in which individuals of the same age group or gender all develop the same eye colour. Domestic animals, by definition, include bird and mammal species artificially selected by humans in the last few thousand years. Humans themselves may have acquired a diverse palette of eye colors, likewise in recent evolutionary time, in the Mesolithic or in the Upper Paleolithic. We posit two previously unrecognized hypotheses regarding eye color variation: 1) eye coloration in wild animals of every species tends to be a fixed trait. 2) Humans and domestic animal populations, on the contrary, have eyes of multiple colors. Sexual selection has been invoked for eye color variation in humans, but this selection mode does not easily apply in domestic animals, where matings are controlled by the human breeder. Eye coloration is polygenic in humans. We wish to investigate the genetics of eye color in other animals, as well as the ecological correlates. Investigating the origin and function of eye colors will shed light on the reason why some species may have either light-colored irises (e.g., white, yellow or light blue) or dark ones (dark red, brown or black). The causes behind the vast array of eye colors across taxa have never been thoroughly investigated, but it may well be that all Darwinian selection processes are at work: sexual selection in humans, artificial selection for domestic animals, and natural selection (mainly) for wild animals.

What Artificial Intelligence Is Doing for Training.

ERIC Educational Resources Information Center

Kirrane, Peter R.; Kirrane, Diane E.

1989-01-01

Discusses the three areas of research and application of artificial intelligence: (1) robotics, (2) natural language processing, and (3) knowledge-based or expert systems. Focuses on what expert systems can do, especially in the area of training. (JOW)

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Laterality and the evolution of the prefronto-cerebellar system in anthropoids.

PubMed

Smaers, Jeroen B; Steele, James; Case, Charleen R; Amunts, Katrin

2013-06-01

There is extensive evidence for an early vertebrate origin of lateralized motor behavior and of related asymmetries in underlying brain systems. We investigate human lateralized motor functioning in a broad comparative context of evolutionary neural reorganization. We quantify evolutionary trends in the fronto-cerebellar system (involved in motor learning) across 46 million years of divergent primate evolution by comparing rates of evolution of prefrontal cortex, frontal motor cortex, and posterior cerebellar hemispheres along individual branches of the primate tree of life. We provide a detailed evolutionary model of the neuroanatomical changes leading to modern human lateralized motor functioning, demonstrating an increased role for the fronto-cerebellar system in the apes dating to their evolutionary divergence from the monkeys (∼30 million years ago (Mya)), and a subsequent shift toward an increased role for prefrontal cortex over frontal motor cortex in the fronto-cerebellar system in the Homo-Pan ancestral lineage (∼10 Mya) and in the human ancestral lineage (∼6 Mya). We discuss these results in the context of cortico-cerebellar functions and their likely role in the evolution of human tool use and speech. © 2013 New York Academy of Sciences.

Deep evolutionary origins of neurobiology

PubMed Central

Mancuso, Stefano

2009-01-01

It is generally assumed, both in common-sense argumentations and scientific concepts, that brains and neurons represent late evolutionary achievements which are present only in more advanced animals. Here we overview recently published data clearly revealing that our understanding of bacteria, unicellular eukaryotic organisms, plants, brains and neurons, rooted in the Aristotelian philosophy is flawed. Neural aspects of biological systems are obvious already in bacteria and unicellular biological units such as sexual gametes and diverse unicellular eukaryotic organisms. Altogether, processes and activities thought to represent evolutionary ‘recent’ specializations of the nervous system emerge rather to represent ancient and fundamental cell survival processes. PMID:19513267

Evolutionary Design and Simulation of a Tube Crawling Inspection Robot

NASA Technical Reports Server (NTRS)

Craft, Michael; Howsman, Tom; ONeil, Daniel; Howell, Joe T. (Technical Monitor)

2002-01-01

The Space Robotics Assembly Team Simulation (SpaceRATS) is an expansive concept that will hopefully lead to a space flight demonstration of a robotic team cooperatively assembling a system from its constitutive parts. A primary objective of the SpaceRATS project is to develop a generalized evolutionary design approach for multiple classes of robots. The portion of the overall SpaceRats program associated with the evolutionary design and simulation of an inspection robot's morphology is the subject of this paper. The vast majority of this effort has concentrated on the use and modification of Darwin2K, a robotic design and simulation software package, to analyze the design of a tube crawling robot. This robot is designed for carrying out inspection duties in relatively inaccessible locations within a liquid rocket engine similar to the SSME. A preliminary design of the tube crawler robot was completed, and the mechanical dynamics of the system were simulated. An evolutionary approach to optimizing a few parameters of the system was utilized, resulting in a more optimum design.

Evolving cell models for systems and synthetic biology.

PubMed

Cao, Hongqing; Romero-Campero, Francisco J; Heeb, Stephan; Cámara, Miguel; Krasnogor, Natalio

2010-03-01

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm's results as well as of the resulting evolved cell models.

Spin Solid versus Magnetic Charge Ordered State in Artificial Honeycomb Lattice of Connected Elements

PubMed Central

Glavic, Artur; Summers, Brock; Dahal, Ashutosh; Kline, Joseph; Van Herck, Walter; Sukhov, Alexander; Ernst, Arthur

2018-01-01

Abstract The nature of magnetic correlation at low temperature in two‐dimensional artificial magnetic honeycomb lattice is a strongly debated issue. While theoretical researches suggest that the system will develop a novel zero entropy spin solid state as T → 0 K, a confirmation to this effect in artificial honeycomb lattice of connected elements is lacking. This study reports on the investigation of magnetic correlation in newly designed artificial permalloy honeycomb lattice of ultrasmall elements, with a typical length of ≈12 nm, using neutron scattering measurements and temperature‐dependent micromagnetic simulations. Numerical modeling of the polarized neutron reflectometry data elucidates the temperature‐dependent evolution of spin correlation in this system. As temperature reduces to ≈7 K, the system tends to develop novel spin solid state, manifested by the alternating distribution of magnetic vortex loops of opposite chiralities. Experimental results are complemented by temperature‐dependent micromagnetic simulations that confirm the dominance of spin solid state over local magnetic charge ordered state in the artificial honeycomb lattice with connected elements. These results enable a direct investigation of novel spin solid correlation in the connected honeycomb geometry of 2D artificial structure. PMID:29721429

Artificial intelligence in process control: Knowledge base for the shuttle ECS model

NASA Technical Reports Server (NTRS)

Stiffler, A. Kent

1989-01-01

The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

Artificial Intelligence and Educational Technology: A Natural Synergy. Extended Abstract.

ERIC Educational Resources Information Center

McCalla, Gordon I.

Educational technology and artificial intelligence (AI) are natural partners in the development of environments to support human learning. Designing systems with the characteristics of a rich learning environment is the long term goal of research in intelligent tutoring systems (ITS). Building these characteristics into a system is extremely…

Artificial Intelligence Methods in Computer-Based Instructional Design. The Minnesota Adaptive Instructional System.

ERIC Educational Resources Information Center

Tennyson, Robert

1984-01-01

Reviews educational applications of artificial intelligence and presents empirically-based design variables for developing a computer-based instruction management system. Taken from a programmatic research effort based on the Minnesota Adaptive Instructional System, variables include amount and sequence of instruction, display time, advisement,…

Artificial Intelligence--Applications in Education.

ERIC Educational Resources Information Center

Poirot, James L.; Norris, Cathleen A.

1987-01-01

This first in a projected series of five articles discusses artificial intelligence and its impact on education. Highlights include the history of artificial intelligence and the impact of microcomputers; learning processes; human factors and interfaces; computer assisted instruction and intelligent tutoring systems; logic programing; and expert…

Surface microstructures of daisy florets (Asteraceae) and characterization of their anisotropic wetting.

PubMed

Koch, Kerstin; Bennemann, Michael; Bohn, Holger F; Albach, Dirk C; Barthlott, Wilhelm

2013-09-01

The surface microstructures on ray florets of 62 species were characterized and compared with modern phylogenetic data of species affiliation in Asteraceae to determine sculptural patterns and their occurrence in the tribes of Asteraceae. Their wettability was studied to identify structural-induced droplet adhesion, which can be used for the development of artificial surfaces for water harvesting and passive surface water transport. The wettability was characterized by contact angle (CA) and tilt angle measurements, performed on fresh ray florets and their epoxy resin replica. The CAs on ray florets varied between 104° and 156°, but water droplets did not roll off when surface was tilted at 90°. Elongated cell structures and cuticle folding orientated in the same direction as the cell elongation caused capillary forces, leading to anisotropic wetting, with extension of water droplets along the length axis of epidermis cells. The strongest elongation of the droplets was also supported by a parallel, cell-overlapping cuticle striation. In artificial surfaces made of epoxy replica of ray florets, this effect was enhanced. The distribution of the identified four structural types exhibits a strong phylogenetic signal and allows the inference of an evolutionary trend in the modification of floret epidermal cells.

Cuttlefish dynamic camouflage: responses to substrate choice and integration of multiple visual cues.

PubMed

Allen, Justine J; Mäthger, Lydia M; Barbosa, Alexandra; Buresch, Kendra C; Sogin, Emilia; Schwartz, Jillian; Chubb, Charles; Hanlon, Roger T

2010-04-07

Prey camouflage is an evolutionary response to predation pressure. Cephalopods have extensive camouflage capabilities and studying them can offer insight into effective camouflage design. Here, we examine whether cuttlefish, Sepia officinalis, show substrate or camouflage pattern preferences. In the first two experiments, cuttlefish were presented with a choice between different artificial substrates or between different natural substrates. First, the ability of cuttlefish to show substrate preference on artificial and natural substrates was established. Next, cuttlefish were offered substrates known to evoke three main camouflage body pattern types these animals show: Uniform or Mottle (function by background matching); or Disruptive. In a third experiment, cuttlefish were presented with conflicting visual cues on their left and right sides to assess their camouflage response. Given a choice between substrates they might encounter in nature, we found no strong substrate preference except when cuttlefish could bury themselves. Additionally, cuttlefish responded to conflicting visual cues with mixed body patterns in both the substrate preference and split substrate experiments. These results suggest that differences in energy costs for different camouflage body patterns may be minor and that pattern mixing and symmetry may play important roles in camouflage.

Human Exploration of Earth's Neighborhood and Mars

NASA Technical Reports Server (NTRS)

Condon, Gerald

2003-01-01

The presentation examines Mars landing scenarios, Earth to Moon transfers comparing direct vs. via libration points. Lunar transfer/orbit diagrams, comparison of opposition class and conjunction class missions, and artificial gravity for human exploration missions. Slides related to Mars landing scenarios include: mission scenario; direct entry landing locations; 2005 opportunity - Type 1; Earth-mars superior conjunction; Lander latitude accessibility; Low thrust - Earth return phase; SEP Earth return sequence; Missions - 200, 2007, 2009; and Mission map. Slides related to Earth to Moon transfers (direct vs. via libration points (L1, L2) include libration point missions, expeditionary vs. evolutionary, Earth-Moon L1 - gateway for lunar surface operations, and Lunar mission libration point vs. lunar orbit rendezvous (LOR). Slides related to lunar transfer/orbit diagrams include: trans-lunar trajectory from ISS parking orbit, trans-Earth trajectories, parking orbit considerations, and landing latitude restrictions. Slides related to comparison of opposition class (short-stay) and conjunction class (long-stay) missions for human exploration of Mars include: Mars mission planning, Earth-Mars orbital characteristics, delta-V variations, and Mars mission duration comparison. Slides related to artificial gravity for human exploration missions include: current configuration, NEP thruster location trades, minor axis rotation, and example load paths.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

PubMed

Kumar, M; Mishra, S K

2017-01-01

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

Two-step evolution of endosymbiosis between hydra and algae.

PubMed

Ishikawa, Masakazu; Shimizu, Hiroshi; Nozawa, Masafumi; Ikeo, Kazuho; Gojobori, Takashi

2016-10-01

In the Hydra vulgaris group, only 2 of the 25 strains in the collection of the National Institute of Genetics in Japan currently show endosymbiosis with green algae. However, whether the other non-symbiotic strains also have the potential to harbor algae remains unknown. The endosymbiotic potential of non-symbiotic strains that can harbor algae may have been acquired before or during divergence of the strains. With the aim of understanding the evolutionary process of endosymbiosis in the H. vulgaris group, we examined the endosymbiotic potential of non-symbiotic strains of the H. vulgaris group by artificially introducing endosymbiotic algae. We found that 12 of the 23 non-symbiotic strains were able to harbor the algae until reaching the grand-offspring through the asexual reproduction by budding. Moreover, a phylogenetic analysis of mitochondrial genome sequences showed that all the strains with endosymbiotic potential grouped into a single cluster (cluster γ). This cluster contained two strains (J7 and J10) that currently harbor algae; however, these strains were not the closest relatives. These results suggest that evolution of endosymbiosis occurred in two steps; first, endosymbiotic potential was gained once in the ancestor of the cluster γ lineage; second, strains J7 and J10 obtained algae independently after the divergence of the strains. By demonstrating the evolution of the endosymbiotic potential in non-symbiotic H. vulgaris group strains, we have clearly distinguished two evolutionary steps. The step-by-step evolutionary process provides significant insight into the evolution of endosymbiosis in cnidarians. Copyright © 2016 Elsevier Inc. All rights reserved.

A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality

NASA Astrophysics Data System (ADS)

Bouter, Anton; Alderliesten, Tanja; Bosman, Peter A. N.

2017-02-01

Taking a multi-objective optimization approach to deformable image registration has recently gained attention, because such an approach removes the requirement of manually tuning the weights of all the involved objectives. Especially for problems that require large complex deformations, this is a non-trivial task. From the resulting Pareto set of solutions one can then much more insightfully select a registration outcome that is most suitable for the problem at hand. To serve as an internal optimization engine, currently used multi-objective algorithms are competent, but rather inefficient. In this paper we largely improve upon this by introducing a multi-objective real-valued adaptation of the recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete optimization. In this work, GOMEA is tailored specifically to the problem of deformable image registration to obtain substantially improved efficiency. This improvement is achieved by exploiting a key strength of GOMEA: iteratively improving small parts of solutions, allowing to faster exploit the impact of such updates on the objectives at hand through partial evaluations. We performed experiments on three registration problems. In particular, an artificial problem containing a disappearing structure, a pair of pre- and post-operative breast CT scans, and a pair of breast MRI scans acquired in prone and supine position were considered. Results show that compared to the previously used evolutionary algorithm, GOMEA obtains a speed-up of up to a factor of 1600 on the tested registration problems while achieving registration outcomes of similar quality.

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

NASA Astrophysics Data System (ADS)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.

Why don’t you use Evolutionary Algorithms in Big Data?

NASA Astrophysics Data System (ADS)

Stanovov, Vladimir; Brester, Christina; Kolehmainen, Mikko; Semenkina, Olga

2017-02-01

In this paper we raise the question of using evolutionary algorithms in the area of Big Data processing. We show that evolutionary algorithms provide evident advantages due to their high scalability and flexibility, their ability to solve global optimization problems and optimize several criteria at the same time for feature selection, instance selection and other data reduction problems. In particular, we consider the usage of evolutionary algorithms with all kinds of machine learning tools, such as neural networks and fuzzy systems. All our examples prove that Evolutionary Machine Learning is becoming more and more important in data analysis and we expect to see the further development of this field especially in respect to Big Data.

Artificial Intelligence and Autonomy: Opportunities and Challenges

DTIC Science & Technology

2017-10-01

Cleared for Public Release Artificial Intelligence & Autonomy Opportunities and Challenges Andrew Ilachinski October 2017 Copyright © 2017 CNA... Artificial Intelligence & Autonomy Opportunities and 5a. CONTRACT NUMBER N00014-16-D-5003 Challenges 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 0605154N...conducted by unmanned and increasingly autonomous weapon systems. This exploratory study considers the state-of-the-art of artificial intelligence (AI