Kosakovsky Pond, Sergei L; Posada, David; Stawiski, Eric; Chappey, Colombe; Poon, Art F Y; Hughes, Gareth; Fearnhill, Esther; Gravenor, Mike B; Leigh Brown, Andrew J; Frost, Simon D W
2009-11-01
Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate
Evolutionary pattern search algorithms
Hart, W.E.
1995-09-19
This paper defines a class of evolutionary algorithms called evolutionary pattern search algorithms (EPSAs) and analyzes their convergence properties. This class of algorithms is closely related to evolutionary programming, evolutionary strategie and real-coded genetic algorithms. EPSAs are self-adapting systems that modify the step size of the mutation operator in response to the success of previous optimization steps. The rule used to adapt the step size can be used to provide a stationary point convergence theory for EPSAs on any continuous function. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods. An experimental analysis of the performance of EPSAs demonstrates that these algorithms can perform a level of global search that is comparable to that of canonical EAs. We also describe a stopping rule for EPSAs, which reliably terminated near stationary points in our experiments. This is the first stopping rule for any class of EAs that can terminate at a given distance from stationary points.
Fast Algorithms for Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan
2005-01-01
Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.
Algorithmic Mechanism Design of Evolutionary Computation
Pei, Yan
2015-01-01
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777
Scheduling Earth Observing Satellites with Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna
2003-01-01
We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.
Automatic design of decision-tree algorithms with evolutionary algorithms.
Barros, Rodrigo C; Basgalupp, Márcio P; de Carvalho, André C P L F; Freitas, Alex A
2013-01-01
This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
A consensus opinion model based on the evolutionary game
NASA Astrophysics Data System (ADS)
Yang, Han-Xin
2016-08-01
We propose a consensus opinion model based on the evolutionary game. In our model, both of the two connected agents receive a benefit if they have the same opinion, otherwise they both pay a cost. Agents update their opinions by comparing payoffs with neighbors. The opinion of an agent with higher payoff is more likely to be imitated. We apply this model in scale-free networks with tunable degree distribution. Interestingly, we find that there exists an optimal ratio of cost to benefit, leading to the shortest consensus time. Qualitative analysis is obtained by examining the evolution of the opinion clusters. Moreover, we find that the consensus time decreases as the average degree of the network increases, but increases with the noise introduced to permit irrational choices. The dependence of the consensus time on the network size is found to be a power-law form. For small or larger ratio of cost to benefit, the consensus time decreases as the degree exponent increases. However, for moderate ratio of cost to benefit, the consensus time increases with the degree exponent. Our results may provide new insights into opinion dynamics driven by the evolutionary game theory.
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Hoist, Terry L.; Pulliam, Thomas H.
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem, both single and two-objective variations, is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Evolutionary Algorithm for Optimal Vaccination Scheme
NASA Astrophysics Data System (ADS)
Parousis-Orthodoxou, K. J.; Vlachos, D. S.
2014-03-01
The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease.
Evolutionary development of path planning algorithms
Hage, M
1998-09-01
This paper describes the use of evolutionary software techniques for developing both genetic algorithms and genetic programs. Genetic algorithms are evolved to solve a specific problem within a fixed and known environment. While genetic algorithms can evolve to become very optimized for their task, they often are very specialized and perform poorly if the environment changes. Genetic programs are evolved through simultaneous training in a variety of environments to develop a more general controller behavior that operates in unknown environments. Performance of genetic programs is less optimal than a specially bred algorithm for an individual environment, but the controller performs acceptably under a wider variety of circumstances. The example problem addressed in this paper is evolutionary development of algorithms and programs for path planning in nuclear environments, such as Chernobyl.
Knowledge Guided Evolutionary Algorithms in Financial Investing
ERIC Educational Resources Information Center
Wimmer, Hayden
2013-01-01
A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…
Protein Structure Prediction with Evolutionary Algorithms
Hart, W.E.; Krasnogor, N.; Pelta, D.A.; Smith, J.
1999-02-08
Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.
Bell-Curve Based Evolutionary Optimization Algorithm
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.
1998-01-01
The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
A Note on Evolutionary Algorithms and Its Applications
ERIC Educational Resources Information Center
Bhargava, Shifali
2013-01-01
This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.
Evolutionary algorithms and multi-agent systems
NASA Astrophysics Data System (ADS)
Oh, Jae C.
2006-05-01
This paper discusses how evolutionary algorithms are related to multi-agent systems and the possibility of military applications using the two disciplines. In particular, we present a game theoretic model for multi-agent resource distribution and allocation where agents in the environment must help each other to survive. Each agent maintains a set of variables representing actual friendship and perceived friendship. The model directly addresses problems in reputation management schemes in multi-agent systems and Peer-to-Peer distributed systems. We present algorithms based on evolutionary game process for maintaining the friendship values as well as a utility equation used in each agent's decision making. For an application problem, we adapted our formal model to the military coalition support problem in peace-keeping missions. Simulation results show that efficient resource allocation and sharing with minimum communication cost is achieved without centralized control.
Adaptive Routing Algorithm in Wireless Communication Networks Using Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Yan, Xuesong; Wu, Qinghua; Cai, Zhihua
At present, mobile communications traffic routing designs are complicated because there are more systems inter-connecting to one another. For example, Mobile Communication in the wireless communication networks has two routing design conditions to consider, i.e. the circuit switching and the packet switching. The problem in the Packet Switching routing design is its use of high-speed transmission link and its dynamic routing nature. In this paper, Evolutionary Algorithms is used to determine the best solution and the shortest communication paths. We developed a Genetic Optimization Process that can help network planners solving the best solutions or the best paths of routing table in wireless communication networks are easily and quickly. From the experiment results can be noted that the evolutionary algorithm not only gets good solutions, but also a more predictable running time when compared to sequential genetic algorithm.
Automated Antenna Design with Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Linden, Derek; Hornby, Greg; Lohn, Jason; Globus, Al; Krishunkumor, K.
2006-01-01
Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to
Predicting polymeric crystal structures by evolutionary algorithms
NASA Astrophysics Data System (ADS)
Zhu, Qiang; Sharma, Vinit; Oganov, Artem R.; Ramprasad, Ramamurthy
2014-10-01
The recently developed evolutionary algorithm USPEX proved to be a tool that enables accurate and reliable prediction of structures. Here we extend this method to predict the crystal structure of polymers by constrained evolutionary search, where each monomeric unit is treated as a building block with fixed connectivity. This greatly reduces the search space and allows the initial structure generation with different sequences and packings of these blocks. The new constrained evolutionary algorithm is successfully tested and validated on a diverse range of experimentally known polymers, namely, polyethylene, polyacetylene, poly(glycolic acid), poly(vinyl chloride), poly(oxymethylene), poly(phenylene oxide), and poly (p-phenylene sulfide). By fixing the orientation of polymeric chains, this method can be further extended to predict the structures of complex linear polymers, such as all polymorphs of poly(vinylidene fluoride), nylon-6 and cellulose. The excellent agreement between predicted crystal structures and experimentally known structures assures a major role of this approach in the efficient design of the future polymeric materials.
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Evolutionary modeling-based approach for model errors correction
NASA Astrophysics Data System (ADS)
Wan, S. Q.; He, W. P.; Wang, L.; Jiang, W.; Zhang, W.
2012-08-01
The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data." On the basis of the intelligent features of evolutionary modeling (EM), including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
Performance Comparison Of Evolutionary Algorithms For Image Clustering
NASA Astrophysics Data System (ADS)
Civicioglu, P.; Atasever, U. H.; Ozkan, C.; Besdok, E.; Karkinli, A. E.; Kesikoglu, A.
2014-09-01
Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
Multilevel and motion model-based ultrasonic speckle tracking algorithms.
Yeung, F; Levinson, S F; Parker, K J
1998-03-01
A multilevel motion model-based approach to ultrasonic speckle tracking has been developed that addresses the inherent trade-offs associated with traditional single-level block matching (SLBM) methods. The multilevel block matching (MLBM) algorithm uses variable matching block and search window sizes in a coarse-to-fine scheme, preserving the relative immunity to noise associated with the use of a large matching block while preserving the motion field detail associated with the use of a small matching block. To decrease further the sensitivity of the multilevel approach to noise, speckle decorrelation and false matches, a smooth motion model-based block matching (SMBM) algorithm has been implemented that takes into account the spatial inertia of soft tissue elements. The new algorithms were compared to SLBM through a series of experiments involving manual translation of soft tissue phantoms, motion field computer simulations of rotation, compression and shear deformation, and an experiment involving contraction of human forearm muscles. Measures of tracking accuracy included mean squared tracking error, peak signal-to-noise ratio (PSNR) and blinded observations of optical flow. Measures of tracking efficiency included the number of sum squared difference calculations and the computation time. In the phantom translation experiments, the SMBM algorithm successfully matched the accuracy of SLBM using both large and small matching blocks while significantly reducing the number of computations and computation time when a large matching block was used. For the computer simulations, SMBM yielded better tracking accuracies and spatial resolution when compared with SLBM using a large matching block. For the muscle experiment, SMBM outperformed SLBM both in terms of PSNR and observations of optical flow. We believe that the smooth motion model-based MLBM approach represents a meaningful development in ultrasonic soft tissue motion measurement. PMID:9587997
Evolutionary algorithm for vehicle driving cycle generation.
Perhinschi, Mario G; Marlowe, Christopher; Tamayo, Sergio; Tu, Jun; Wayne, W Scott
2011-09-01
Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.
Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm
Lagos, Carolina; Crawford, Broderick; Cabrera, Enrique; Rubio, José-Miguel; Paredes, Fernando
2014-01-01
Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric. PMID:25254257
A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms
Díaz-Manríquez, Alan; Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar
2016-01-01
Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class. PMID:27382366
Learning evasive maneuvers using evolutionary algorithms and neural networks
NASA Astrophysics Data System (ADS)
Kang, Moung Hung
In this research, evolutionary algorithms and recurrent neural networks are combined to evolve control knowledge to help pilots avoid being struck by a missile, based on a two-dimensional air combat simulation model. The recurrent neural network is used for representing the pilot's control knowledge and evolutionary algorithms (i.e., Genetic Algorithms, Evolution Strategies, and Evolutionary Programming) are used for optimizing the weights and/or topology of the recurrent neural network. The simulation model of the two-dimensional evasive maneuver problem evolved is used for evaluating the performance of the recurrent neural network. Five typical air combat conditions were selected to evaluate the performance of the recurrent neural networks evolved by the evolutionary algorithms. Analysis of Variance (ANOVA) tests and response graphs were used to analyze the results. Overall, there was little difference in the performance of the three evolutionary algorithms used to evolve the control knowledge. However, the number of generations of each algorithm required to obtain the best performance was significantly different. ES converges the fastest, followed by EP and then by GA. The recurrent neural networks evolved by the evolutionary algorithms provided better performance than the traditional recommendations for evasive maneuvers, maximum gravitational turn, for each air combat condition. Furthermore, the recommended actions of the recurrent neural networks are reasonable and can be used for pilot training.
Biased Randomized Algorithm for Fast Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Williams, Colin; Vartan, Farrokh
2005-01-01
A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the
PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.
Örtenberg, A; Magnusson, M; Sandborg, M; Alm Carlsson, G; Malusek, A
2016-06-01
New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained.
PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.
Örtenberg, A; Magnusson, M; Sandborg, M; Alm Carlsson, G; Malusek, A
2016-06-01
New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. PMID:26454270
Using Evolutionary Algorithms to Induce Oblique Decision Trees
Cantu-Paz, E.; Kamath, C.
2000-01-21
This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolutionary strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find Competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of training instances.
Development of antibiotic regimens using graph based evolutionary algorithms.
Corns, Steven M; Ashlock, Daniel A; Bryden, Kenneth M
2013-12-01
This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems.
A novel fitness evaluation method for evolutionary algorithms
NASA Astrophysics Data System (ADS)
Wang, Ji-feng; Tang, Ke-zong
2013-03-01
Fitness evaluation is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. But these algorithms may require huge computation power for solving nonlinear programming problems. This paper proposes a novel fitness evaluation approach which employs similarity-base learning embedded in a classical differential evolution (SDE) to evaluate all new individuals. Each individual consists of three elements: parameter vector (v), a fitness value (f), and a reliability value(r). The f is calculated using NFEA, and only when the r is below a threshold is the f calculated using true fitness function. Moreover, applying error compensation system to the proposed algorithm further enhances the performance of the algorithm to make r much closer to true fitness value for each new child. Simulation results over a comprehensive set of benchmark functions show that the convergence rate of the proposed algorithm is much faster than much that of the compared algorithms.
Comparison of evolutionary algorithms for LPDA antenna optimization
NASA Astrophysics Data System (ADS)
Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.
2016-08-01
A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.
Models based on "out-of Kilter" algorithm
NASA Astrophysics Data System (ADS)
Adler, M. J.; Drobot, R.
2012-04-01
In case of many water users along the river stretches, it is very important, in case of low flows and droughty periods to develop an optimization model for water allocation, to cover all needs under certain predefined constraints, depending of the Contingency Plan for drought management. Such a program was developed during the implementation of the WATMAN Project, in Romania (WATMAN Project, 2005-2006, USTDA) for Arges-Dambovita-Ialomita Basins water transfers. This good practice was proposed for WATER CoRe Project- Good Practice Handbook for Drought Management, (InterregIVC, 2011), to be applied for the European Regions. Two types of simulation-optimization models based on an improved version of out-of-kilter algorithm as optimization technique have been developed and used in Romania: • models for founding of the short-term operation of a WMS, • models generically named SIMOPT that aim to the analysis of long-term WMS operation and have as the main results the statistical WMS functional parameters. A real WMS is modeled by an arcs-nodes network so the real WMS operation problem becomes a problem of flows in networks. The nodes and oriented arcs as well as their characteristics such as lower and upper limits and associated costs are the direct analog of the physical and operational WMS characteristics. Arcs represent both physical and conventional elements of WMS such as river branches, channels or pipes, water user demands or other water management requirements, trenches of water reservoirs volumes, water levels in channels or rivers, nodes are junctions of at least two arcs and stand for locations of lakes or water reservoirs and/or confluences of river branches, water withdrawal or wastewater discharge points, etc. Quantitative features of water resources, water users and water reservoirs or other water works are expressed as constraints of non-violating the lower and upper limits assigned on arcs. Options of WMS functioning i.e. water retention/discharge in
A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.
Li, Shan; Kang, Liying; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Jirapong, Peeraool; Ongsakul, Weerakorn
2008-10-01
This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
Li, Shan; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969
Optimization of media by evolutionary algorithms for production of polyols.
Patil, S V; Jayaraman, V K; Kulkarni, B D
2002-01-01
Biotransformation of sucrose-based medium to polyols has been reported for the first time using osmophilic yeast, Hansenula anomala. A new, real coded evolutionary algorithm was developed for optimization of fermentation medium in parallel shake-flask experiments. By iteratively employing the nature-inspired techniques of selection, crossover, and mutation for a fixed number of generations, the algorithm obtains the optimal values of important process variables, namely, inoculum size and sugar, yeast extract, urea, and MgSO4 concentrations. Maximum polyols yield of 76.43% has been achieved. The method is useful for reducing the overall development time to obtain an efficient fermentation process. PMID:12396116
A filter-based evolutionary algorithm for constrained optimization.
Clevenger, Lauren M.; Hart, William Eugene; Ferguson, Lauren Ann
2004-02-01
We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.
Evolutionary algorithm for optimization of nonimaging Fresnel lens geometry.
Yamada, N; Nishikawa, T
2010-06-21
In this study, an evolutionary algorithm (EA), which consists of genetic and immune algorithms, is introduced to design the optical geometry of a nonimaging Fresnel lens; this lens generates the uniform flux concentration required for a photovoltaic cell. Herein, a design procedure that incorporates a ray-tracing technique in the EA is described, and the validity of the design is demonstrated. The results show that the EA automatically generated a unique geometry of the Fresnel lens; the use of this geometry resulted in better uniform flux concentration with high optical efficiency.
Receiver diversity combining using evolutionary algorithms in Rayleigh fading channel.
Akbari, Mohsen; Manesh, Mohsen Riahi; El-Saleh, Ayman A; Reza, Ahmed Wasif
2014-01-01
In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods.
Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel
Akbari, Mohsen; Manesh, Mohsen Riahi
2014-01-01
In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods. PMID:25045725
Hart, W.E.
1999-02-10
Evolutionary programs (EPs) and evolutionary pattern search algorithms (EPSAS) are two general classes of evolutionary methods for optimizing on continuous domains. The relative performance of these methods has been evaluated on standard global optimization test functions, and these results suggest that EPSAs more robustly converge to near-optimal solutions than EPs. In this paper we evaluate the relative performance of EPSAs and EPs on a real-world application: flexible ligand binding in the Autodock docking software. We compare the performance of these methods on a suite of docking test problems. Our results confirm that EPSAs and EPs have comparable performance, and they suggest that EPSAs may be more robust on larger, more complex problems.
Multi-objective Job Shop Rescheduling with Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Hao, Xinchang; Gen, Mitsuo
In current manufacturing systems, production processes and management are involved in many unexpected events and new requirements emerging constantly. This dynamic environment implies that operation rescheduling is usually indispensable. A wide variety of procedures and heuristics has been developed to improve the quality of rescheduling. However, most proposed approaches are derived usually with respect to simplified assumptions. As a consequence, these approaches might be inconsistent with the actual requirements in a real production environment, i.e., they are often unsuitable and inflexible to respond efficiently to the frequent changes. In this paper, a multi-objective job shop rescheduling problem (moJSRP) is formulated to improve the practical application of rescheduling. To solve the moJSRP model, an evolutionary algorithm is designed, in which a random key-based representation and interactive adaptive-weight (i-awEA) fitness assignment are embedded. To verify the effectiveness, the proposed algorithm has been compared with other apporaches and benchmarks on the robustness of moJRP optimziation. The comparison results show that iAWGA-A is better than weighted fitness method in terms of effectiveness and stability. Simlarly, iAWGA-A also outperforms other well stability approachessuch as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm2 (SPEA2).
Optimal classification of standoff bioaerosol measurements using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Nyhavn, Ragnhild; Moen, Hans J. F.; Farsund, Øystein; Rustad, Gunnar
2011-05-01
Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.
Kaptein, B L; Valstar, E R; Stoel, B C; Rozing, P M; Reiber, J H C
2004-01-01
Model-based roentgen stereophotogrammetric analysis (RSA) uses a three-dimensional surface model of an implant in order to estimate accurately the pose of that implant from a stereo pair of roentgen images. The technique is based on minimization of the difference between the actually projected contour of an implant and the virtually projected contour of a model of that same implant. The advantage of model-based RSA over conventional marker-based RSA is that it is not necessary to attach markers to the implant. In this paper, three pose estimation algorithms for model-based RSA are evaluated. The algorithms were assessed on the basis of their sensitivities to noise in the actual contour, to the amount of drop-outs in the actual contour, to the number of points in the actual contour and to shrinkage or expansion of the actual contour. The algorithms that were studied are the iterative inverse perspective matching (IIPM) algorithm, an algorithm based on minimization of the difference (DIF) between the actual contour and the virtual contour, and an algorithm based on minimization of the non-overlapping area (NOA) between the actual and virtual contour. The results of the simulation and phantom experiments show that the NOA algorithm does not fulfil the high accuracy that is necessary for model-based RSA. The IIPM and DIF algorithms are robust to the different distortions, making model-based RSA a possible replacement for marker-based RSA.
Virus evolutionary genetic algorithm for task collaboration of logistics distribution
NASA Astrophysics Data System (ADS)
Ning, Fanghua; Chen, Zichen; Xiong, Li
2005-12-01
In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.
A Review of Evolutionary Algorithms for Data Mining
NASA Astrophysics Data System (ADS)
Freitas, Alex A.
Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.
The algorithmic anatomy of model-based evaluation
Daw, Nathaniel D.; Dayan, Peter
2014-01-01
Despite many debates in the first half of the twentieth century, it is now largely a truism that humans and other animals build models of their environments and use them for prediction and control. However, model-based (MB) reasoning presents severe computational challenges. Alternative, computationally simpler, model-free (MF) schemes have been suggested in the reinforcement learning literature, and have afforded influential accounts of behavioural and neural data. Here, we study the realization of MB calculations, and the ways that this might be woven together with MF values and evaluation methods. There are as yet mostly only hints in the literature as to the resulting tapestry, so we offer more preview than review. PMID:25267820
An evolutionary algorithm for interval solid transportation problems.
Jiménez, F; Verdegay, J L
1999-01-01
The Solid Transportation Problem arises when bounds are given on three item properties. Usually, these properties are source, destination and mode of transport (conveyance), and may be given in an interval way. This paper deals with solid transportation problems in which the data in the constraint set are expressed in an interval form, i.e. when sources, destinations and conveyances have interval values instead of point values. An arbitrary linear or nonlinear objective function is also considered. To solve the problem, an Evolutionary Algorithm which extends and generalizes other approaches considering only point values, is proposed.
Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization
HART,WILLIAM E.
2000-06-01
The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.
Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms
NASA Astrophysics Data System (ADS)
Cervone, Guido; Franzese, Pasquale; Grajdeanu, Adrian
2010-10-01
The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source. The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
Maximizing Submodular Functions under Matroid Constraints by Evolutionary Algorithms.
Friedrich, Tobias; Neumann, Frank
2015-01-01
Many combinatorial optimization problems have underlying goal functions that are submodular. The classical goal is to find a good solution for a given submodular function f under a given set of constraints. In this paper, we investigate the runtime of a simple single objective evolutionary algorithm called (1 + 1) EA and a multiobjective evolutionary algorithm called GSEMO until they have obtained a good approximation for submodular functions. For the case of monotone submodular functions and uniform cardinality constraints, we show that the GSEMO achieves a (1 - 1/e)-approximation in expected polynomial time. For the case of monotone functions where the constraints are given by the intersection of K ≥ 2 matroids, we show that the (1 + 1) EA achieves a (1/k + δ)-approximation in expected polynomial time for any constant δ > 0. Turning to nonmonotone symmetric submodular functions with k ≥ 1 matroid intersection constraints, we show that the GSEMO achieves a 1/((k + 2)(1 + ε))-approximation in expected time O(n(k + 6)log(n)/ε.
Efficiency of Evolutionary Algorithms for Calibration of Watershed Models
NASA Astrophysics Data System (ADS)
Ahmadi, M.; Arabi, M.
2009-12-01
Since the promulgation of the Clean Water Act in the U.S. and other similar legislations around the world over the past three decades, watershed management programs have focused on the nexus of pollution prevention and mitigation. In this context, hydrologic/water quality models have been increasingly embedded in the decision making process. Simulation models are now commonly used to investigate the hydrologic response of watershed systems under varying climatic and land use conditions, and also to study the fate and transport of contaminants at various spatiotemporal scales. Adequate calibration and corroboration of models for various outputs at varying scales is an essential component of watershed modeling. The parameter estimation process could be challenging when multiple objectives are important. For example, improving streamflow predictions of the model at a stream location may result in degradation of model predictions for sediments and/or nutrient at the same location or other outlets. This paper aims to evaluate the applicability and efficiency of single and multi objective evolutionary algorithms for parameter estimation of complex watershed models. To this end, the Shuffled Complex Evolution (SCE-UA) algorithm, a single-objective genetic algorithm (GA), and a multi-objective genetic algorithm (i.e., NSGA-II) were reconciled with the Soil and Water Assessment Tool (SWAT) to calibrate the model at various locations within the Wildcat Creek Watershed, Indiana. The efficiency of these methods were investigated using different error statistics including root mean square error, coefficient of determination and Nash-Sutcliffe efficiency coefficient for the output variables as well as the baseflow component of the stream discharge. A sensitivity analysis was carried out to screening model parameters that bear significant uncertainties. Results indicated that while flow processes can be reasonably ascertained, parameterization of nutrient and pesticide processes
Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2001-01-01
A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.
Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
2005-01-01
This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.
An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy
Zhang, Yu-Xian; Qian, Xiao-Yi; Peng, Hui-Deng; Wang, Jian-Hui
2016-01-01
For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And Hε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy. PMID:27057159
Optimizing quantum gas production by an evolutionary algorithm
NASA Astrophysics Data System (ADS)
Lausch, T.; Hohmann, M.; Kindermann, F.; Mayer, D.; Schmidt, F.; Widera, A.
2016-05-01
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a ^{87}rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
Aguirre, Juan; Giannoula, Alexia; Minagawa, Taisuke; Funk, Lutz; Turon, Pau; Durduran, Turgut
2013-01-01
A model based reconstruction algorithm that exploits translational symmetries for photoacoustic microscopy to drastically reduce the memory cost is presented. The memory size needed to store the model matrix is independent of the number of acquisitions at different positions. This helps us to overcome one of the main limitations of previous algorithms. Furthermore, using the algebraic reconstruction technique and building the model matrix “on the fly”, we have obtained fast reconstructions of simulated and experimental data on both two- and three-dimensional grids using a traditional dark field photoacoustic microscope and a standard personal computer. PMID:24409382
EVO—Evolutionary algorithm for crystal structure prediction
NASA Astrophysics Data System (ADS)
Bahmann, Silvia; Kortus, Jens
2013-06-01
We present EVO—an evolution strategy designed for crystal structure search and prediction. The concept and main features of biological evolution such as creation of diversity and survival of the fittest have been transferred to crystal structure prediction. EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it. Running EVO with different numbers of carbon atoms per unit cell yields graphite as the lowest energy structure as well as a diamond-like structure, both in one run. Our implementation also supports the search for 2D structures and was able to find a boron sheet with structural features so far not considered in literature. Program summaryProgram title: EVO Catalogue identifier: AEOZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License version 3 No. of lines in distributed program, including test data, etc.: 23488 No. of bytes in distributed program, including test data, etc.: 1830122 Distribution format: tar.gz Programming language: Python. Computer: No limitations known. Operating system: Linux. RAM: Negligible compared to the requirements of the electronic structure programs used Classification: 7.8. External routines: Quantum ESPRESSO (http://www.quantum-espresso.org/), GULP (https://projects.ivec.org/gulp/) Nature of problem: Crystal structure search is a global optimisation problem in 3N+3 dimensions where N is the number of atoms in the unit cell. The high dimensional search space is accompanied by an unknown energy landscape. Solution method: Evolutionary algorithms transfer the main features of biological evolution to use them in global searches. The combination of the "survival of the fittest" (deterministic) and the
NASA Astrophysics Data System (ADS)
Marwati, Rini; Yulianti, Kartika; Pangestu, Herny Wulandari
2016-02-01
A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm.
Allhoff, K T; Ritterskamp, D; Rall, B C; Drossel, B; Guill, C
2015-06-04
The networks of predator-prey interactions in ecological systems are remarkably complex, but nevertheless surprisingly stable in terms of long term persistence of the system as a whole. In order to understand the mechanism driving the complexity and stability of such food webs, we developed an eco-evolutionary model in which new species emerge as modifications of existing ones and dynamic ecological interactions determine which species are viable. The food-web structure thereby emerges from the dynamical interplay between speciation and trophic interactions. The proposed model is less abstract than earlier evolutionary food web models in the sense that all three evolving traits have a clear biological meaning, namely the average body mass of the individuals, the preferred prey body mass, and the width of their potential prey body mass spectrum. We observed networks with a wide range of sizes and structures and high similarity to natural food webs. The model networks exhibit a continuous species turnover, but massive extinction waves that affect more than 50% of the network are not observed.
NASA Astrophysics Data System (ADS)
Allhoff, K. T.; Ritterskamp, D.; Rall, B. C.; Drossel, B.; Guill, C.
2015-06-01
The networks of predator-prey interactions in ecological systems are remarkably complex, but nevertheless surprisingly stable in terms of long term persistence of the system as a whole. In order to understand the mechanism driving the complexity and stability of such food webs, we developed an eco-evolutionary model in which new species emerge as modifications of existing ones and dynamic ecological interactions determine which species are viable. The food-web structure thereby emerges from the dynamical interplay between speciation and trophic interactions. The proposed model is less abstract than earlier evolutionary food web models in the sense that all three evolving traits have a clear biological meaning, namely the average body mass of the individuals, the preferred prey body mass, and the width of their potential prey body mass spectrum. We observed networks with a wide range of sizes and structures and high similarity to natural food webs. The model networks exhibit a continuous species turnover, but massive extinction waves that affect more than 50% of the network are not observed.
Ju, Ying; Zhang, Songming; Ding, Ningxiang; Zeng, Xiangxiang; Zhang, Xingyi
2016-01-01
The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising. PMID:27670156
Model-based spectral estimation of Doppler signals using parallel genetic algorithms.
Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F
2000-05-01
Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods. PMID:10767617
Model-based spectral estimation of Doppler signals using parallel genetic algorithms.
Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F
2000-05-01
Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.
Sys, K; Boon, N; Verstraete, W
2004-06-01
A flexible, extendable tool for the optimization of (micro)biological processes and protocols using evolutionary algorithms was developed. It has been tested using three different theoretical optimization problems: 2 two-dimensional problems, one with three maxima and one with five maxima and a river autopurification optimization problem with boundary conditions. For each problem, different evolutionary parameter settings were used for the optimization. For each combination of evolutionary parameters, 15 generations were run 20 times. It has been shown that in all cases, the evolutionary algorithm gave rise to valuable results. Generally, the algorithms were able to detect the more stable sub-maximum even if there existed less stable maxima. The latter is, from a practical point of view, generally more desired. The most important factors influencing the convergence process were the parameter value randomization rate and distribution. The developed software, described in this work, is available for free.
NASA Astrophysics Data System (ADS)
Tang, Zhili
2016-06-01
This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.
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.
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation.
A novel evolutionary approach for optimizing content-based image indexing algorithms.
Saadatmand-Tarzjan, Mahdi; Moghaddam, Hamid Abrishami
2007-02-01
Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.
Yeguas, Enrique; Joan-Arinyo, Robert; Victoria Luz N, Mar A
2011-01-01
The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model would compute an index of the quality of the solution reached by the algorithm as a function of run-time. Conversely, if we fix an index of quality for the solution, the model would give the number of iterations to be expected. In this work, we develop a statistical model to describe the performance of PBIL and CHC evolutionary algorithms applied to solve the root identification problem. This problem is basic in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The performance model is empirically validated over a benchmark with very large search spaces.
EvoOligo: oligonucleotide probe design with multiobjective evolutionary algorithms.
Shin, Soo-Yong; Lee, In-Hee; Cho, Young-Min; Yang, Kyung-Ae; Zhang, Byoung-Tak
2009-12-01
Probe design is one of the most important tasks in successful deoxyribonucleic acid microarray experiments. We propose a multiobjective evolutionary optimization method for oligonucleotide probe design based on the multiobjective nature of the probe design problem. The proposed multiobjective evolutionary approach has several distinguished features, compared with previous methods. First, the evolutionary approach can find better probe sets than existing simple filtering methods with fixed threshold values. Second, the multiobjective approach can easily incorporate the user's custom criteria or change the existing criteria. Third, our approach tries to optimize the combination of probes for the given set of genes, in contrast to other tools that independently search each gene for qualifying probes. Lastly, the multiobjective optimization method provides various sets of probe combinations, among which the user can choose, depending on the target application. The proposed method is implemented as a platform called EvoOligo and is available for service on the web. We test the performance of EvoOligo by designing probe sets for 19 types of Human Papillomavirus and 52 genes in the Arabidopsis Calmodulin multigene family. The design results from EvoOligo are proven to be superior to those from well-known existing probe design tools, such as OligoArray and OligoWiz.
Sutton, Andrew M; Neumann, Frank; Nallaperuma, Samadhi
2014-01-01
Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We investigate the structural properties in TSP instances that influence the optimization process of evolutionary algorithms and use this information to bound their runtime. We analyze the runtime in dependence of the number of inner points k. In the first part of the paper, we study a [Formula: see text] EA in a strictly black box setting and show that it can solve the Euclidean TSP in expected time [Formula: see text] where A is a function of the minimum angle [Formula: see text] between any three points. Based on insights provided by the analysis, we improve this upper bound by introducing a mixed mutation strategy that incorporates both 2-opt moves and permutation jumps. This strategy improves the upper bound to [Formula: see text]. In the second part of the paper, we use the information gained in the analysis to incorporate domain knowledge to design two fixed-parameter tractable (FPT) evolutionary algorithms for the planar Euclidean TSP. We first develop a [Formula: see text] EA based on an analysis by M. Theile, 2009, "Exact solutions to the traveling salesperson problem by a population-based evolutionary algorithm," Lecture notes in computer science, Vol. 5482 (pp. 145-155), that solves the TSP with k inner points in [Formula: see text] generations with probability [Formula: see text]. We then design a [Formula: see text] EA that incorporates a dynamic programming step into the fitness evaluation. We prove that a variant of this evolutionary algorithm using 2-opt mutation solves the problem after [Formula: see text] steps in expectation with a cost of [Formula: see text] for each fitness evaluation.
Optimization of aeroelastic composite structures using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Manan, A.; Vio, G. A.; Harmin, M. Y.; Cooper, J. E.
2010-02-01
The flutter/divergence speed of a simple rectangular composite wing is maximized through the use of different ply orientations. Four different biologically inspired optimization algorithms (binary genetic algorithm, continuous genetic algorithm, particle swarm optimization, and ant colony optimization) and a simple meta-modeling approach are employed statistically on the same problem set. In terms of the best flutter speed, it was found that similar results were obtained using all of the methods, although the continuous methods gave better answers than the discrete methods. When the results were considered in terms of the statistical variation between different solutions, ant colony optimization gave estimates with much less scatter.
A multiobjective evolutionary algorithm to find community structures based on affinity propagation
NASA Astrophysics Data System (ADS)
Shang, Ronghua; Luo, Shuang; Zhang, Weitong; Stolkin, Rustam; Jiao, Licheng
2016-07-01
Community detection plays an important role in reflecting and understanding the topological structure of complex networks, and can be used to help mine the potential information in networks. This paper presents a Multiobjective Evolutionary Algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection. Firstly, APMOEA takes the method of affinity propagation (AP) to initially divide the network. To accelerate its convergence, the multiobjective evolutionary algorithm selects nondominated solutions from the preliminary partitioning results as its initial population. Secondly, the multiobjective evolutionary algorithm finds solutions approximating the true Pareto optimal front through constantly selecting nondominated solutions from the population after crossover and mutation in iterations, which overcomes the tendency of data clustering methods to fall into local optima. Finally, APMOEA uses an elitist strategy, called "external archive", to prevent degeneration during the process of searching using the multiobjective evolutionary algorithm. According to this strategy, the preliminary partitioning results obtained by AP will be archived and participate in the final selection of Pareto-optimal solutions. Experiments on benchmark test data, including both computer-generated networks and eight real-world networks, show that the proposed algorithm achieves more accurate results and has faster convergence speed compared with seven other state-of-art algorithms.
Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach
NASA Technical Reports Server (NTRS)
Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)
2002-01-01
We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.
Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm
Chambers, D; Lehman, S; Dowla, F
2007-05-17
A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.
First principles prediction of amorphous phases using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Nahas, Suhas; Gaur, Anshu; Bhowmick, Somnath
2016-07-01
We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ˜2% of those reported by ab initio MD calculations and experimental studies.
First principles prediction of amorphous phases using evolutionary algorithms.
Nahas, Suhas; Gaur, Anshu; Bhowmick, Somnath
2016-07-01
We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ∼2% of those reported by ab initio MD calculations and experimental studies. PMID:27394098
Experiments with a Parallel Multi-Objective Evolutionary Algorithm for Scheduling
NASA Technical Reports Server (NTRS)
Brown, Matthew; Johnston, Mark D.
2013-01-01
Evolutionary multi-objective algorithms have great potential for scheduling in those situations where tradeoffs among competing objectives represent a key requirement. One challenge, however, is runtime performance, as a consequence of evolving not just a single schedule, but an entire population, while attempting to sample the Pareto frontier as accurately and uniformly as possible. The growing availability of multi-core processors in end user workstations, and even laptops, has raised the question of the extent to which such hardware can be used to speed up evolutionary algorithms. In this paper we report on early experiments in parallelizing a Generalized Differential Evolution (GDE) algorithm for scheduling long-range activities on NASA's Deep Space Network. Initial results show that significant speedups can be achieved, but that performance does not necessarily improve as more cores are utilized. We describe our preliminary results and some initial suggestions from parallelizing the GDE algorithm. Directions for future work are outlined.
Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.
Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard
2012-06-01
We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems. PMID:22697525
On source models for 192Ir HDR brachytherapy dosimetry using model based algorithms
NASA Astrophysics Data System (ADS)
Pantelis, Evaggelos; Zourari, Kyveli; Zoros, Emmanouil; Lahanas, Vasileios; Karaiskos, Pantelis; Papagiannis, Panagiotis
2016-06-01
A source model is a prerequisite of all model based dose calculation algorithms. Besides direct simulation, the use of pre-calculated phase space files (phsp source models) and parameterized phsp source models has been proposed for Monte Carlo (MC) to promote efficiency and ease of implementation in obtaining photon energy, position and direction. In this work, a phsp file for a generic 192Ir source design (Ballester et al 2015) is obtained from MC simulation. This is used to configure a parameterized phsp source model comprising appropriate probability density functions (PDFs) and a sampling procedure. According to phsp data analysis 15.6% of the generated photons are absorbed within the source, and 90.4% of the emergent photons are primary. The PDFs for sampling photon energy and direction relative to the source long axis, depend on the position of photon emergence. Photons emerge mainly from the cylindrical source surface with a constant probability over ±0.1 cm from the center of the 0.35 cm long source core, and only 1.7% and 0.2% emerge from the source tip and drive wire, respectively. Based on these findings, an analytical parameterized source model is prepared for the calculation of the PDFs from data of source geometry and materials, without the need for a phsp file. The PDFs from the analytical parameterized source model are in close agreement with those employed in the parameterized phsp source model. This agreement prompted the proposal of a purely analytical source model based on isotropic emission of photons generated homogeneously within the source core with energy sampled from the 192Ir spectrum, and the assignment of a weight according to attenuation within the source. Comparison of single source dosimetry data obtained from detailed MC simulation and the proposed analytical source model show agreement better than 2% except for points lying close to the source longitudinal axis.
Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms
Bosl, W J
2005-01-26
The systems approach to genomics seeks quantitative and predictive descriptions of cells and organisms. However, both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms. A key aspect to all biological problems, including environmental microbiology, evolution of infectious diseases, and the adaptation of cancer cells is the evolvability of genomes. This is particularly important for Genomes to Life missions, which tend to focus on the prospect of engineering microorganisms to achieve desired goals in environmental remediation and climate change mitigation, and energy production. All of these will require quantitative tools for understanding the evolvability of organisms. Laboratory biodefense goals will need quantitative tools for predicting complicated host-pathogen interactions and finding counter-measures. In this project, we seek to develop methods to simulate how external and internal signals cause the genetic apparatus to adapt and organize to produce complex biochemical systems to achieve survival. This project is specifically directed toward building a computational methodology for simulating the adaptability of genomes. This project investigated the feasibility of using a novel quantitative approach to studying the adaptability of genomes and biochemical pathways. This effort was intended to be the preliminary part of a larger, long-term effort between key leaders in computational and systems biology at Harvard University and LLNL, with Dr. Bosl as the lead PI. Scientific goals for the long-term project include the development and testing of new hypotheses to explain the observed adaptability of yeast biochemical pathways when the myosin-II gene is deleted and the development of a novel data-driven evolutionary computation as a way to connect exploratory computational simulation with hypothesis
NASA Astrophysics Data System (ADS)
Della Mora, S.; Boschi, L.; Becker, T. W.; Giardini, D.
2010-12-01
The wavelength spectrum of three-dimensional (3D) heterogeneity naturally reflects the nature of Earth dynamics, and is in its own right an important constraint for geodynamical modeling. The Earth's spectrum has been usually evaluated indirectly, on the basis of previously derived tomographic models. If the geographic distribution of seismic heterogeneities is neglected, however, one can invert global seismic data directly to find the spectrum of the Earth. Inverting for the spectrum is in principle (fewer unknowns) cheaper and robust than inverting for the 3D structure of a planet: this should allow us to constrain planetary structure at smaller scales than by current 3D models. Based on the work of Gudmundsson and coworkers in the early 1990s, we have developed a linear algorithm for surface waves. The spectra we obtain are in qualitative agreement with results from 3D tomography, but the resolving power is generally lower, due to the simplifications required to linearise the ``spectral'' inversion. To overcome this problem, we performed full nonlinear inversions of synthetically generated and real datasets, and compare the obtained spectra with the input and tomographic models respectively. The inversions are calculated on a distributed memory parallel nodes cluster, employing the MPI package. An evolutionary strategy approach is used to explore the parameter space, using the PIKAIA software. The first preliminary results show a resolving power higher than that of linearised inversion. This confirms that the approximations required in the linear formulation affect the solution quality, and suggests that the nonlinear approach might effectively help to constrain the heterogeneity spectrum more robustly than currently possible.
Wayne F. Boyer; Gurdeep S. Hura
2005-09-01
The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized task orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,
Kwan, Mei-Po; Xiao, Ningchuan; Ding, Guoxiang
2015-01-01
Due to the complexity and multidimensional characteristics of human activities, assessing the similarity of human activity patterns and classifying individuals with similar patterns remains highly challenging. This paper presents a new and unique methodology for evaluating the similarity among individual activity patterns. It conceptualizes multidimensional sequence alignment (MDSA) as a multiobjective optimization problem, and solves this problem with an evolutionary algorithm. The study utilizes sequence alignment to code multiple facets of human activities into multidimensional sequences, and to treat similarity assessment as a multiobjective optimization problem that aims to minimize the alignment cost for all dimensions simultaneously. A multiobjective optimization evolutionary algorithm (MOEA) is used to generate a diverse set of optimal or near-optimal alignment solutions. Evolutionary operators are specifically designed for this problem, and a local search method also is incorporated to improve the search ability of the algorithm. We demonstrate the effectiveness of our method by comparing it with a popular existing method called ClustalG using a set of 50 sequences. The results indicate that our method outperforms the existing method for most of our selected cases. The multiobjective evolutionary algorithm presented in this paper provides an effective approach for assessing activity pattern similarity, and a foundation for identifying distinctive groups of individuals with similar activity patterns. PMID:26190858
On Polymorphic Circuits and Their Design Using Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Stoica, Adrian; Zebulum, Ricardo; Keymeulen, Didier; Lohn, Jason; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper introduces the concept of polymorphic electronics (polytronics) - referring to electronics with superimposed built-in functionality. A function change does not require switches/reconfiguration as in traditional approaches. Instead the change comes from modifications in the characteristics of devices involved in the circuit, in response to controls such as temperature, power supply voltage (VDD), control signals, light, etc. The paper illustrates polytronic circuits in which the control is done by temperature, morphing signals, and VDD respectively. Polytronic circuits are obtained by evolutionary design/evolvable hardware techniques. These techniques are ideal for the polytronics design, a new area that lacks design guidelines, know-how,- yet the requirements/objectives are easy to specify and test. The circuits are evolved/synthesized in two different modes. The first mode explores an unstructured space, in which transistors can be interconnected freely in any arrangement (in simulations only). The second mode uses a Field Programmable Transistor Array (FPTA) model, and the circuit topology is sought as a mapping onto a programmable architecture (these experiments are performed both in simulations and on FPTA chips). The experiments demonstrated the synthesis. of polytronic circuits by evolution. The capacity of storing/hiding "extra" functions provides for watermark/invisible functionality, thus polytronics may find uses in intelligence/security applications.
Nonlinear model-based control algorithm for a distillation column using software sensor.
Jana, Amiya Kumar; Samanta, Amar Nath; Ganguly, Saibal
2005-04-01
This paper presents the design of model-based globally linearizing control (GLC) structure for a distillation process within the differential geometric framework. The model of a nonideal binary distillation column, whose characteristics were highly nonlinear and strongly interactive, is used as a real process. The classical GLC law is comprised of a transformer (input-output linearizing state feedback), a nonlinear state observer, and an external PI controller. The tray temperature based short-cut observer (TTBSCO) has been used as a state estimator within the control structure, in which all tray temperatures were considered to be measured. Accordingly, the liquid phase composition of each tray was calculated online using the derived temperature-composition correlation. In the simulation experiment, the proposed GLC coupled with TTBSCO (GLC-TTBSCO) outperformed a conventional PI controller based on servo performances with and without measurement noise as well as on regulatory behaviors. In the subsequent part, the GLC law has been synthesized in conjunction with tray temperature based reduced-order observer (GLC-TTBROO) where the distillate and bottom compositions of the distillation process have been inferred from top and bottom product temperatures respectively, which were measured online. Finally, the comparative performance of the GLC-TTBSCO and the GLC-TTBROO has been addressed under parametric uncertainty and the GLC-TTBSCO algorithm provided slightly better performance than the GLC-TTBROO. The resulting control laws are rather general and can be easily adopted for other binary distillation columns.
NASA Astrophysics Data System (ADS)
Malusek, Alexandr; Magnusson, Maria; Sandborg, Michael; Westin, Robin; Alm Carlsson, Gudrun
2014-03-01
Better knowledge of elemental composition of patient tissues may improve the accuracy of absorbed dose delivery in brachytherapy. Deficiencies of water-based protocols have been recognized and work is ongoing to implement patient-specific radiation treatment protocols. A model based iterative image reconstruction algorithm DIRA has been developed by the authors to automatically decompose patient tissues to two or three base components via dual-energy computed tomography. Performance of an updated version of DIRA was evaluated for the determination of prostate calcification. A computer simulation using an anthropomorphic phantom showed that the mass fraction of calcium in the prostate tissue was determined with accuracy better than 9%. The calculated mass fraction was little affected by the choice of the material triplet for the surrounding soft tissue. Relative differences between true and approximated values of linear attenuation coefficient and mass energy absorption coefficient for the prostate tissue were less than 6% for photon energies from 1 keV to 2 MeV. The results indicate that DIRA has the potential to improve the accuracy of dose delivery in brachytherapy despite the fact that base material triplets only approximate surrounding soft tissues.
A novel non-model-based 6-DOF electromagnetic tracking method using non-iterative algorithm.
Ge, Xin; Lai, Dakun; Wu, Xiaomei; Fang, Zuxiang
2009-01-01
Electromagnetic tracking, a non-fluoroscopic image navigation method most often used in minimally invasive therapy, has prominent advantages and features over the traditional X-ray radioscopy. Using two rotating coils and one 3-axis magnetic sensor, a novel 6 degree of freedom (DOF) electromagnetic tracking method is proposed in this paper. Two alternate rotating magnetic fields are generated in turns by these coils and the moving-around sensor simultaneously detects the magnetic filed flux density in 3 orthogonal directions. As the magnitude of a magnetic field comes to the maximum only when the rotating coil directly points toward the sensor, the spatial position and orientation of the sensor can be determined using triangulation measurement. An embodiment and the corresponding system framework of this method are developed and a non-model-based non-iterative algorithm is presented to calculate the 6-DOF of position and orientation. Moreover, simulation experiments are performed to validate the proposed method. The obtained results show that the averaged position error is 0.2365 cm and the averaged orientation error is below 1 degree away from low resolution area.
Runtime Analysis of (1+1) Evolutionary Algorithm for a TSP Instance
NASA Astrophysics Data System (ADS)
Zhang, Yu Shan; Hao, Zhi Feng
Evolutionary Algorithms (EAs) have been used widely and successfully in solving a famous classical combinatorial optimization problem-the traveling salesman problem (TSP). There are lots of experimental results concerning the TSP. However, relatively few theoretical results on the runtime analysis of EAs on the TSP are available. This paper conducts a runtime analysis of a simple Evolutionary Algorithm called (1+1) EA on a TSP instance. We represent a tour as a string of integer, and randomly choose 2-opt and 3-opt operator as the mutation operator at each iteration. The expected runtime of (1+1) EA on this TSP instance is proved to be O(n 4), which is tighter than O(n 6 + (1/ρ)nln n) of (1+1) MMAA (Max-Min ant algorithms). It is also shown that the selection of mutation operator is very important in (1+1) EA.
Application of an evolutionary algorithm in the optimal design of micro-sensor.
Lu, Qibing; Wang, Pan; Guo, Sihai; Sheng, Buyun; Liu, Xingxing; Fan, Zhun
2015-01-01
This paper introduces an automatic bond graph design method based on genetic programming for the evolutionary design of micro-resonator. First, the system-level behavioral model is discussed, which based on genetic programming and bond graph. Then, the geometry parameters of components are automatically optimized, by using the genetic algorithm with constraints. To illustrate this approach, a typical device micro-resonator is designed as an example in biomedicine. This paper provides a new idea for the automatic optimization design of biomedical sensors by evolutionary calculation.
Gutjahr, Walter J
2012-01-01
For stochastic multi-objective combinatorial optimization (SMOCO) problems, the adaptive Pareto sampling (APS) framework has been proposed, which is based on sampling and on the solution of deterministic multi-objective subproblems. We show that when plugging in the well-known simple evolutionary multi-objective optimizer (SEMO) as a subprocedure into APS, ε-dominance has to be used to achieve fast convergence to the Pareto front. Two general theorems are presented indicating how runtime complexity results for APS can be derived from corresponding results for SEMO. This may be a starting point for the runtime analysis of evolutionary SMOCO algorithms.
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
THE APPLICATION OF AN EVOLUTIONARY ALGORITHM TO THE OPTIMIZATION OF A MESOSCALE METEOROLOGICAL MODEL
Werth, D.; O'Steen, L.
2008-02-11
We show that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root mean square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). We find that the optimization can be efficient with relatively modest computer resources, thus operational implementation is possible. The optimization efficiency, however, is found to depend strongly on the procedure used to perturb the 'child' parameters relative to their 'parents' within the evolutionary algorithm. In addition, the meteorological variables included in the rms error and their weighting are found to be an important factor with respect to finding the global optimum.
Graff, Mario; Poli, Riccardo; Flores, Juan J
2013-01-01
Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program-induction algorithms (EPAs), we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models. PMID:23136918
Graff, Mario; Poli, Riccardo; Flores, Juan J
2013-01-01
Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program-induction algorithms (EPAs), we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models.
Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets
NASA Astrophysics Data System (ADS)
Smolinski, Tomasz G.; Newell, Trevor; McDaniel, Samantha; Pokrajac, David
2013-09-01
Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set of additive components, which then can be used to build a classifier capable of recognizing typical, but yet unseen trajectories, and identifying those that seem suspicious.
Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Craig, Sam; While, Lyndon; Barone, Luigi
We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.
Efficiency Enhancement Of Helix Traveling Wave Tube Based On ɛMulti-Objective Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Li, Guo-Chao; Liu, Pu-Kun; Xiao, Liu; Hao, Bao-Liang
2010-04-01
A two-dimensional non-linear helix traveling wave tube (TWT) theory in frequency-domain is described in this article and the theory is integrated with ɛ Multi-Objective Evolutionary Algorithm(MOEA). The efficiency optimization of a helix TWT with phase taper has been done. The optimization results was verified by a three dimensional nonlinear model. The results show the feasibility and reliability of the optimization algorithm and the two-dimensional nonlinear theory. The integrated program can provide calculable parameters for the design of helix TWT. It can also accelerate development process and reduce test costs.
An Evolutionary Algorithm for Improved Diversity in DSL Spectrum Balancing Solutions
NASA Astrophysics Data System (ADS)
Bezerra, Johelden; Klautau, Aldebaro; Monteiro, Marcio; Pelaes, Evaldo; Medeiros, Eduardo; Dortschy, Boris
2010-12-01
There are many spectrum balancing algorithms to combat the deleterious impact of crosstalk interference in digital subscriber lines (DSL) networks. These algorithms aim to find a unique operating point by optimizing the power spectral densities (PSDs) of the modems. Typically, the figure of merit of this optimization is the bit rate, power consumption or margin. This work poses and solves a different problem: instead of providing the solution for one specific operation point, it finds a set of operating points, each one corresponding to a distinct matrix with PSDs. This solution is useful for planning DSL deployment, for example, helping operators to conveniently evaluate their network capabilities and better plan their usage. The proposed method is based on a multiobjective formulation and implemented as an evolutionary genetic algorithm. Simulation results show that this algorithm achieves a better diversity among the operating points with lower computational cost when compared to an alternative approach.
NASA Astrophysics Data System (ADS)
Shirazi, Abolfazl
2016-10-01
This article introduces a new method to optimize finite-burn orbital manoeuvres based on a modified evolutionary algorithm. Optimization is carried out based on conversion of the orbital manoeuvre into a parameter optimization problem by assigning inverse tangential functions to the changes in direction angles of the thrust vector. The problem is analysed using boundary delimitation in a common optimization algorithm. A method is introduced to achieve acceptable values for optimization variables using nonlinear simulation, which results in an enlarged convergence domain. The presented algorithm benefits from high optimality and fast convergence time. A numerical example of a three-dimensional optimal orbital transfer is presented and the accuracy of the proposed algorithm is shown.
Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm
NASA Astrophysics Data System (ADS)
Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung
2016-07-01
In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.
A statistical model-based algorithm for `black-box' multi-objective optimisation
NASA Astrophysics Data System (ADS)
Žilinskas, Antanas
2014-01-01
The problem of multi-objective optimisation with 'expensive' 'black-box' objective functions is considered. An algorithm is proposed that generalises the single objective P-algorithm constructed using the statistical model of multimodal functions and concepts of the theory of rational decisions under uncertainty. Computational examples are included demonstrating that the algorithm proposed possess several expected properties.
Technology Transfer Automated Retrieval System (TEKTRAN)
Hyperspectral scattering is a promising technique for rapid and noninvasive measurement of multiple quality attributes of apple fruit. A hierarchical evolutionary algorithm (HEA) approach, in combination with subspace decomposition and partial least squares (PLS) regression, was proposed to select o...
Evolutionary algorithm based offline/online path planner for UAV navigation.
Nikolos, I K; Valavanis, K P; Tsourveloudis, N C; Kostaras, A N
2003-01-01
An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.
NASA Astrophysics Data System (ADS)
Zatarain Salazar, Jazmin; Reed, Patrick M.; Herman, Jonathan D.; Giuliani, Matteo; Castelletti, Andrea
2016-06-01
Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ɛ-MOEA, the auto-adaptive Borg MOEA, and ɛ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.
Li, Miqing; Yang, Shengxiang; Zheng, Jinhua; Liu, Xiaohui
2014-01-01
The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.
Design and Optimization of Low-thrust Orbit Transfers Using Q-law and Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Lee, Seungwon; vonAllmen, Paul; Fink, Wolfgang; Petropoulos, Anastassios; Terrile, Richard
2005-01-01
Future space missions will depend more on low-thrust propulsion (such as ion engines) thanks to its high specific impulse. Yet, the design of low-thrust trajectories is complex and challenging. Third-body perturbations often dominate the thrust, and a significant change to the orbit requires a long duration of thrust. In order to guide the early design phases, we have developed an efficient and efficacious method to obtain approximate propellant and flight-time requirements (i.e., the Pareto front) for orbit transfers. A search for the Pareto-optimal trajectories is done in two levels: optimal thrust angles and locations are determined by Q-law, while the Q-law is optimized with two evolutionary algorithms: a genetic algorithm and a simulated-annealing-related algorithm. The examples considered are several types of orbit transfers around the Earth and the asteroid Vesta.
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.
Anticipation versus adaptation in Evolutionary Algorithms: The case of Non-Stationary Clustering
NASA Astrophysics Data System (ADS)
González, A. I.; Graña, M.; D'Anjou, A.; Torrealdea, F. J.
1998-07-01
From the technological point of view is usually more important to ensure the ability to react promptly to changing environmental conditions than to try to forecast them. Evolution Algorithms were proposed initially to drive the adaptation of complex systems to varying or uncertain environments. In the general setting, the adaptive-anticipatory dilemma reduces itself to the placement of the interaction with the environment in the computational schema. Adaptation consists of the estimation of the proper parameters from present data in order to react to a present environment situation. Anticipation consists of the estimation from present data in order to react to a future environment situation. This duality is expressed in the Evolutionary Computation paradigm by the precise location of the consideration of present data in the computation of the individuals fitness function. In this paper we consider several instances of Evolutionary Algorithms applied to precise problem and perform an experiment that test their response as anticipative and adaptive mechanisms. The non stationary problem considered is that of Non Stationary Clustering, more precisely the adaptive Color Quantization of image sequences. The experiment illustrates our ideas and gives some quantitative results that may support the proposition of the Evolutionary Computation paradigm for other tasks that require the interaction with a Non-Stationary environment.
Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller
NASA Astrophysics Data System (ADS)
Kopka, Ryszard
2014-12-01
This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.
Generation of multi-million element meshes for solid model-based geometries: The Dicer algorithm
Melander, D.J.; Benzley, S.E.; Tautges, T.J.
1997-06-01
The Dicer algorithm generates a fine mesh by refining each element in a coarse all-hexahedral mesh generated by any existing all-hexahedral mesh generation algorithm. The fine mesh is geometry-conforming. Using existing all-hexahedral meshing algorithms to define the initial coarse mesh simplifies the overall meshing process and allows dicing to take advantage of improvements in other meshing algorithms immediately. The Dicer algorithm will be used to generate large meshes in support of the ASCI program. The authors also plan to use dicing as the basis for parallel mesh generation. Dicing strikes a careful balance between the interactive mesh generation and multi-million element mesh generation processes for complex 3D geometries, providing an efficient means for producing meshes of varying refinement once the coarse mesh is obtained.
O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.
2012-01-01
Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279
Tuning of MEMS Gyroscope using Evolutionary Algorithm and "Switched Drive-Angle" Method
NASA Technical Reports Server (NTRS)
Keymeulen, Didier; Ferguson, Michael I.; Breuer, Luke; Peay, Chris; Oks, Boris; Cheng, Yen; Kim, Dennis; MacDonald, Eric; Foor, David; Terrile, Rich; Yee, Karl
2006-01-01
We propose a tuning method for Micro-Electro-Mechanical Systems (MEMS) gyroscopes based on evolutionary computation that has the capacity to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of increased sensitivity. We present the results of an experiment to determine the speed and efficiency of an evolutionary algorithm applied to electrostatic tuning of MEMS micro gyros. The MEMS gyro used in this experiment is a pyrex post resonator gyro (PRG) in a closed-loop control system. A measure of the quality of tuning is given by the difference in resonant frequencies, or frequency split, for the two orthogonal rocking axes. The current implementation of the closed-loop platform is able to measure and attain a relative stability in the sub-millihertz range, leading to a reduction of the frequency split to less than 100 mHz.
A Self-adaptive Evolutionary Algorithm for Multi-objective Optimization
NASA Astrophysics Data System (ADS)
Cao, Ruifen; Li, Guoli; Wu, Yican
Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation,P c and P m , are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.
Optimising operational amplifiers by evolutionary algorithms and gm/Id method
NASA Astrophysics Data System (ADS)
Tlelo-Cuautle, E.; Sanabria-Borbon, A. C.
2016-10-01
The evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is applied herein in the optimisation of operational transconductance amplifiers. NSGA-II is accelerated by applying the gm/Id method to estimate reduced search spaces associated to widths (W) and lengths (L) of the metal-oxide-semiconductor field-effect-transistor (MOSFETs), and to guarantee their appropriate bias levels conditions. In addition, we introduce an integer encoding for the W/L sizes of the MOSFETs to avoid a post-processing step for rounding-off their values to be multiples of the integrated circuit fabrication technology. Finally, from the feasible solutions generated by NSGA-II, we introduce a second optimisation stage to guarantee that the final feasible W/L sizes solutions support process, voltage and temperature (PVT) variations. The optimisation results lead us to conclude that the gm/Id method and integer encoding are quite useful to accelerate the convergence of the evolutionary algorithm NSGA-II, while the second optimisation stage guarantees robustness of the feasible solutions to PVT variations.
A multi-objective evolutionary algorithm for protein structure prediction with immune operators.
Judy, M V; Ravichandran, K S; Murugesan, K
2009-08-01
Genetic algorithms (GA) are often well suited for optimisation problems involving several conflicting objectives. It is more suitable to model the protein structure prediction problem as a multi-objective optimisation problem since the potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms) and experiments have shown that those types of interactions are in conflict, by using the potential energy function, Chemistry at Harvard Macromolecular Mechanics. In this paper, we have modified the immune inspired Pareto archived evolutionary strategy (I-PAES) algorithm and denoted it as MI-PAES. It can effectively exploit some prior knowledge about the hydrophobic interactions, which is one of the most important driving forces in protein folding to make vaccines. The proposed MI-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and the computational time and often results in much better search ability than that of the canonical GA.
A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry.
Makin, Alexis D J; Bertamini, Marco; Jones, Andrew; Holmes, Tim; Zanker, Johannes M
2016-03-01
Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation-symmetry, DS gene) and orientation (0° to 90°, orientation, ORI gene). An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference.
A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry
Bertamini, Marco; Jones, Andrew; Holmes, Tim; Zanker, Johannes M.
2016-01-01
Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation–symmetry, DS gene) and orientation (0° to 90°, orientation, ORI gene). An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference. PMID:27433324
A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry.
Makin, Alexis D J; Bertamini, Marco; Jones, Andrew; Holmes, Tim; Zanker, Johannes M
2016-03-01
Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation-symmetry, DS gene) and orientation (0° to 90°, orientation, ORI gene). An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference. PMID:27433324
NASA Astrophysics Data System (ADS)
Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad
2008-04-01
To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology
A model-based 3D phase unwrapping algorithm using Gegenbauer polynomials.
Langley, Jason; Zhao, Qun
2009-09-01
The application of a two-dimensional (2D) phase unwrapping algorithm to a three-dimensional (3D) phase map may result in an unwrapped phase map that is discontinuous in the direction normal to the unwrapped plane. This work investigates the problem of phase unwrapping for 3D phase maps. The phase map is modeled as a product of three one-dimensional Gegenbauer polynomials. The orthogonality of Gegenbauer polynomials and their derivatives on the interval [-1, 1] are exploited to calculate the expansion coefficients. The algorithm was implemented using two well-known Gegenbauer polynomials: Chebyshev polynomials of the first kind and Legendre polynomials. Both implementations of the phase unwrapping algorithm were tested on 3D datasets acquired from a magnetic resonance imaging (MRI) scanner. The first dataset was acquired from a homogeneous spherical phantom. The second dataset was acquired using the same spherical phantom but magnetic field inhomogeneities were introduced by an external coil placed adjacent to the phantom, which provided an additional burden to the phase unwrapping algorithm. Then Gaussian noise was added to generate a low signal-to-noise ratio dataset. The third dataset was acquired from the brain of a human volunteer. The results showed that Chebyshev implementation and the Legendre implementation of the phase unwrapping algorithm give similar results on the 3D datasets. Both implementations of the phase unwrapping algorithm compare well to PRELUDE 3D, 3D phase unwrapping software well recognized for functional MRI. PMID:19671967
A model-based 3D phase unwrapping algorithm using Gegenbauer polynomials
NASA Astrophysics Data System (ADS)
Langley, Jason; Zhao, Qun
2009-09-01
The application of a two-dimensional (2D) phase unwrapping algorithm to a three-dimensional (3D) phase map may result in an unwrapped phase map that is discontinuous in the direction normal to the unwrapped plane. This work investigates the problem of phase unwrapping for 3D phase maps. The phase map is modeled as a product of three one-dimensional Gegenbauer polynomials. The orthogonality of Gegenbauer polynomials and their derivatives on the interval [-1, 1] are exploited to calculate the expansion coefficients. The algorithm was implemented using two well-known Gegenbauer polynomials: Chebyshev polynomials of the first kind and Legendre polynomials. Both implementations of the phase unwrapping algorithm were tested on 3D datasets acquired from a magnetic resonance imaging (MRI) scanner. The first dataset was acquired from a homogeneous spherical phantom. The second dataset was acquired using the same spherical phantom but magnetic field inhomogeneities were introduced by an external coil placed adjacent to the phantom, which provided an additional burden to the phase unwrapping algorithm. Then Gaussian noise was added to generate a low signal-to-noise ratio dataset. The third dataset was acquired from the brain of a human volunteer. The results showed that Chebyshev implementation and the Legendre implementation of the phase unwrapping algorithm give similar results on the 3D datasets. Both implementations of the phase unwrapping algorithm compare well to PRELUDE 3D, 3D phase unwrapping software well recognized for functional MRI.
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Creating ensembles of oblique decision trees with evolutionary algorithms and sampling
Cantu-Paz, Erick; Kamath, Chandrika
2006-06-13
A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.
Searching for the Optimal Working Point of the MEIC at JLab Using an Evolutionary Algorithm
Balsa Terzic, Matthew Kramer, Colin Jarvis
2011-03-01
The Medium-energy Electron Ion Collider (MEIC), a proposed medium-energy ring-ring electron-ion collider based on CEBAF at Jefferson Lab. The collider luminosity and stability are sensitive to the choice of a working point - the betatron and synchrotron tunes of the two colliding beams. Therefore, a careful selection of the working point is essential for stable operation of the collider, as well as for achieving high luminosity. Here we describe a novel approach for locating an optimal working point based on evolutionary algorithm techniques.
Jiang, Shouyong; Yang, Shengxiang
2016-02-01
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng
2014-01-01
Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design
NASA Astrophysics Data System (ADS)
Jeong, S.; Obayashi, S.; Minemura, Y.
2008-01-01
A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem—the geometry of diesel engine combustion chamber reducing exhaust emissions such as NOx, soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate that the volume near the lower central part of the combustion chamber has a large effect on exhaust emissions and the optimum chamber geometry will vary depending on fuel injection angle.
Frutos, M.; Méndez, M.; Tohmé, F.; Broz, D.
2013-01-01
Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier. PMID:24489502
Frutos, M; Méndez, M; Tohmé, F; Broz, D
2013-01-01
Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier. PMID:24489502
NASA Astrophysics Data System (ADS)
Weatheritt, Jack; Sandberg, Richard
2016-11-01
This paper presents a novel and promising approach to turbulence model formulation, rather than putting forward a particular new model. Evolutionary computation has brought symbolic regression of scalar fields into the domain of algorithms and this paper describes a novel expansion of Gene Expression Programming for the purpose of tensor modeling. By utilizing high-fidelity data and uncertainty measures, mathematical models for tensors are created. The philosophy behind the framework is to give freedom to the algorithm to produce a constraint-free model; its own functional form that was not previously imposed. Turbulence modeling is the target application, specifically the improvement of separated flow prediction. Models are created by considering the anisotropy of the turbulent stress tensor and formulating non-linear constitutive stress-strain relationships. A previously unseen flow field is computed and compared to the baseline linear model and an established non-linear model of comparable complexity. The results are highly encouraging.
Microcellular propagation prediction model based on an improved ray tracing algorithm.
Liu, Z-Y; Guo, L-X; Fan, T-Q
2013-11-01
Two-dimensional (2D)/two-and-one-half-dimensional ray tracing (RT) algorithms for the use of the uniform theory of diffraction and geometrical optics are widely used for channel prediction in urban microcellular environments because of their high efficiency and reliable prediction accuracy. In this study, an improved RT algorithm based on the "orientation face set" concept and on the improved 2D polar sweep algorithm is proposed. The goal is to accelerate point-to-point prediction, thereby making RT prediction attractive and convenient. In addition, the use of threshold control of each ray path and the handling of visible grid points for reflection and diffraction sources are adopted, resulting in an improved efficiency of coverage prediction over large areas. Measured results and computed predictions are also compared for urban scenarios. The results indicate that the proposed prediction model works well and is a useful tool for microcellular communication applications.
Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine
2012-12-09
Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a
NASA Technical Reports Server (NTRS)
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
A Cooperative Co-Evolutionary Genetic Algorithm for Tree Scoring and Ancestral Genome Inference.
Gao, Nan; Zhang, Yan; Feng, Bing; Tang, Jijun
2015-01-01
Recent advances of technology have made it easy to obtain and compare whole genomes. Rearrangements of genomes through operations such as reversals and transpositions are rare events that enable researchers to reconstruct deep evolutionary history among species. Some of the popular methods need to search a large tree space for the best scored tree, thus it is desirable to have a fast and accurate method that can score a given tree efficiently. During the tree scoring procedure, the genomic structures of internal tree nodes are also provided, which provide important information for inferring ancestral genomes and for modeling the evolutionary processes. However, computing tree scores and ancestral genomes are very difficult and a lot of researchers have to rely on heuristic methods which have various disadvantages. In this paper, we describe the first genetic algorithm for tree scoring and ancestor inference, which uses a fitness function considering co-evolution, adopts different initial seeding methods to initialize the first population pool, and utilizes a sorting-based approach to realize evolution. Our extensive experiments show that compared with other existing algorithms, this new method is more accurate and can infer ancestral genomes that are much closer to the true ancestors. PMID:26671797
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V
2013-01-01
The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
David J. Muth Jr.
2006-09-01
This paper examines the use of graph based evolutionary algorithms (GBEAs) to find multiple acceptable solutions for heat transfer in engineering systems during the optimization process. GBEAs are a type of evolutionary algorithm (EA) in which a topology, or geography, is imposed on an evolving population of solutions. The rates at which solutions can spread within the population are controlled by the choice of topology. As in nature geography can be used to develop and sustain diversity within the solution population. Altering the choice of graph can create a more or less diverse population of potential solutions. The choice of graph can also affect the convergence rate for the EA and the number of mating events required for convergence. The engineering system examined in this paper is a biomass fueled cookstove used in developing nations for household cooking. In this cookstove wood is combusted in a small combustion chamber and the resulting hot gases are utilized to heat the stove’s cooking surface. The spatial temperature profile of the cooking surface is determined by a series of baffles that direct the flow of hot gases. The optimization goal is to find baffle configurations that provide an even temperature distribution on the cooking surface. Often in engineering, the goal of optimization is not to find the single optimum solution but rather to identify a number of good solutions that can be used as a starting point for detailed engineering design. Because of this a key aspect of evolutionary optimization is the diversity of the solutions found. The key conclusion in this paper is that GBEA’s can be used to create multiple good solutions needed to support engineering design.
An Evolutionary Algorithm with Double-Level Archives for Multiobjective Optimization.
Chen, Ni; Chen, Wei-Neng; Gong, Yue-Jiao; Zhan, Zhi-Hui; Zhang, Jun; Li, Yun; Tan, Yu-Song
2015-09-01
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed. PMID:25343775
Ishibuchi, Hisao; Sudo, Takahiko; Nojima, Yusuke
2016-01-01
In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a ([Formula: see text])ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the ([Formula: see text])ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm. PMID:27026888
XTALOPT: An open-source evolutionary algorithm for crystal structure prediction
NASA Astrophysics Data System (ADS)
Lonie, David C.; Zurek, Eva
2011-02-01
The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely
Meta-Model Based Optimisation Algorithms for Robust Optimization of 3D Forging Sequences
Fourment, Lionel
2007-04-07
In order to handle costly and complex 3D metal forming optimization problems, we develop a new optimization algorithm that allows finding satisfactory solutions within less than 50 iterations (/function evaluation) in the presence of local extrema. It is based on the sequential approximation of the problem objective function by the Meshless Finite Difference Method (MFDM). This changing meta-model allows taking into account the gradient information, if available, or not. It can be easily extended to take into account uncertainties on the optimization parameters. This new algorithm is first evaluated on analytic functions, before being applied to a 3D forging benchmark, the preform tool shape optimization that allows minimizing the potential of fold formation during the two-stepped forging sequence.
[Research of Feedback Algorithm and Deformable Model Based on Improved Spring-mass Model].
Chen, Weidong; Chen, Panpan; Zhu, Qiguang
2015-10-01
A new diamond-based variable spring-mass model has been proposed in this study. It can realize the deformation simulation for different organs by changing the length of the springs, spring coefficient and initial angle. The virtual spring joined in the model is used to provide constraint and to avoid hyperelastic phenomenon when excessive force appears. It is also used for the calculation of force feedback in the deformation process. With the deformation force feedback algorithm, we calculated the deformation area of each layer through screening effective particles, and contacted the deformation area with the force. This simplified the force feedback algorithm of traditional spring-particle model. The deformation simulation was realized by the PHANTOM haptic interaction devices based on this model. The experimental results showed that the model had the advantage of simple structure and of being easy to implement. The deformation force feedback algorithm reduces the number of the deformation calculation, improves the real-time deformation and has a more realistic deformation effect.
Blind watermark algorithm on 3D motion model based on wavelet transform
NASA Astrophysics Data System (ADS)
Qi, Hu; Zhai, Lang
2013-12-01
With the continuous development of 3D vision technology, digital watermark technology, as the best choice for copyright protection, has fused with it gradually. This paper proposed a blind watermark plan of 3D motion model based on wavelet transform, and made it loaded into the Vega real-time visual simulation system. Firstly, put 3D model into affine transform, and take the distance from the center of gravity to the vertex of 3D object in order to generate a one-dimensional discrete signal; then make this signal into wavelet transform to change its frequency coefficients and embed watermark, finally generate 3D motion model with watermarking. In fixed affine space, achieve the robustness in translation, revolving and proportion transforms. The results show that this approach has better performances not only in robustness, but also in watermark- invisibility.
NASA Astrophysics Data System (ADS)
Franke, Timothy; Weadon, Tim; Ford, John; Garcia-Sanz, Mario
2015-10-01
Various forms of measurement errors limit telescope tracking performance in practice. A new method for identifying the correcting coefficients for encoder interpolation error is developed. The algorithm corrects the encoder measurement by identifying a harmonic model of the system and using that model to compute the necessary correction parameters. The approach improves upon others by explicitly modeling the unknown dynamics of the structure and controller and by not requiring a separate system identification to be performed. Experience gained from pin-pointing the source of encoder error on the Green Bank Radio Telescope (GBT) is presented. Several tell-tale indicators of encoder error are discussed. Experimental data from the telescope, tested with two different encoders, are presented. Demonstration of the identification methodology on the GBT as well as details of its implementation are discussed. A root mean square tracking error reduction from 0.68 arc seconds to 0.21 arc sec was achieved by changing encoders and was further reduced to 0.10 arc sec with the calibration algorithm. In particular, the ubiquity of this error source is shown and how, by careful correction, it is possible to go beyond the advertised accuracy of an encoder.
NASA Astrophysics Data System (ADS)
Liu, Jintao; Chen, Xi; Zhang, Xingnan; Hoagland, Kyle D.
2012-04-01
Recently developed hillslope storage dynamics theory can represent the essential physical behavior of a natural system by accounting explicitly for the plan shape of a hillslope in an elegant and simple way. As a result, this theory is promising for improving catchment-scale hydrologic modeling. In this study, grid digital elevation model (DEM) based algorithms for determination of hillslope geometric characteristics (e.g., hillslope units and width functions in hillslope storage dynamics models) are presented. This study further develops a method for hillslope partitioning, established by Fan and Bras (1998), by applying it on a grid network. On the basis of hillslope unit derivation, a flow distance transforms method (TD∞) is suggested in order to decrease the systematic error of grid DEM-based flow distance calculation caused by flow direction approximation to streamlines. Hillslope width transfer functions are then derived to convert the probability density functions of flow distance into hillslope width functions. These algorithms are applied and evaluated on five abstract hillslopes, and detailed tests and analyses are carried out by comparing the derivation results with theoretical width functions. The results demonstrate that the TD∞ improves estimations of the flow distance and thus hillslope width function. As the proposed procedures are further applied in a natural catchment, we find that the natural hillslope width function can be well fitted by the Gaussian function. This finding is very important for applying the newly developed hillslope storage dynamics models in a real catchment.
An evolutionary algorithm for the segmentation of muscles and bones of the lower limb.
NASA Astrophysics Data System (ADS)
Lpez, Marco A.; Braidot, A.; Sattler, Anbal; Schira, Claudia; Uriburu, E.
2016-04-01
In the field of medical image segmentation, muscles segmentation is a problem that has not been fully resolved yet. This is due to the fact that the basic assumption of image segmentation, which asserts that a visual distinction should ex- ist between the different structures to be identified, is infringed. As the tissue composition of two different muscles is the same, it becomes extremely difficult to distinguish one another if they are near. We have developed an evolutionary algorithm which selects the set and the sequence of morphological operators that better segments muscles and bones from an MRI image. The achieved results shows that the developed algorithm presents average sensitivity values close to 75% in the segmentation of the different processed muscles and bones. It also presents average specificity values close to 93% for the same structures. Furthermore, the algorithm can identify muscles that are closely located through the path from their origin point to their insertions, with very low error values (below 7%) .
Geometric model-based fitting algorithm for orientation-selective PELDOR data
NASA Astrophysics Data System (ADS)
Abdullin, Dinar; Hagelueken, Gregor; Hunter, Robert I.; Smith, Graham M.; Schiemann, Olav
2015-03-01
Pulsed electron-electron double resonance (PELDOR or DEER) spectroscopy is frequently used to determine distances between spin centres in biomacromolecular systems. Experiments where mutual orientations of the spin pair are selectively excited provide the so-called orientation-selective PELDOR data. This data is characterised by the orientation dependence of the modulation depth parameter and of the dipolar frequencies. This dependence has to be taken into account in the data analysis in order to extract distance distributions accurately from the experimental time traces. In this work, a fitting algorithm for such data analysis is discussed. The approach is tested on PELDOR data-sets from the literature and is compared with the previous results.
NASA Astrophysics Data System (ADS)
Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas
2016-02-01
In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.
Chen, Wei-Chen; Maitra, Ranjan
2011-01-01
We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithm (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.
Dietrich, Arne; Haider, Hilde
2015-08-01
Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.
Zhang, Zhen; Bedder, Matthew; Smith, Stephen L; Walker, Dawn; Shabir, Saqib; Southgate, Jennifer
2016-08-01
This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models. PMID:27267455
Improvement of the Analysis of the Peroxy Radicals Using AN Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Just, Gabriel M. P.; Rupper, Patrick; Miller, Terry A.; Meerts, W. Leo
2009-06-01
For quite awhile, our laboratory has had interestin the organic peroxy radicals which are relevant to atmospheric chemistry as well as low temperature combustion. We first studied these radicals via room temperature cavity ringdown spectroscopy (CRDS). We continued our investigation of the same radicals using a quasi-Fourier-transform laser source using a supersonic jet expansion in order to obtain partially rotationally resolved spectra which are nearly doppler limited. In order to analyze our spectra we decided to complement our conventional least-square-fit method of simulating spectra by using a evolutionary algorithm (EA) approach which uses both the frequency and the intensity information that are contained in our dense and complicated spectra. This presentation will focus on the CD_3O_2 spectrum to demonstrate the capabilities and the quality of the fits obtained via the EA method and compare it with the traditional least-square-fit method.
NASA Astrophysics Data System (ADS)
Bekele, Elias G.; Nicklow, John W.
2005-10-01
This paper explores the role of landscapes in generating ecosystem services while maximizing gross margin associated with agricultural commodity production. Ecosystem services considered include the reduction of nonpoint source pollutants such as sediment, phosphorous, and nitrogen yields from a watershed. The analysis relies on an integrative modeling framework that combines a comprehensive watershed model (SWAT) with a multiobjective evolutionary algorithm (SPEA2). Application of the resulting model to a watershed in southern Illinois demonstrates the effectiveness of the approach in providing tradeoff solutions between gross margin and the generation of ecosystem services. These solutions are important to policy makers and planners in that they provide information about the cost-effectiveness of alternative agricultural landscapes.
NASA Technical Reports Server (NTRS)
Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)
2002-01-01
A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.
Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm
NASA Astrophysics Data System (ADS)
Wu, Xiaolan; Grubesic, Tony H.
2010-12-01
Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.
NASA Astrophysics Data System (ADS)
Olson, C. C.; Nichols, J. M.; Michalowicz, J. V.; Bucholtz, F.
2011-06-01
This work describes an approach for efficiently shaping the response characteristics of a fixed dynamical system by forcing with a designed input. We obtain improved inputs by using an evolutionary algorithm to search a space of possible waveforms generated by a set of nonlinear, ordinary differential equations (ODEs). Good solutions are those that result in a desired system response subject to some input efficiency constraint, such as signal power. In particular, we seek to find inputs that best disrupt a phase-locked loop (PLL). Three sets of nonlinear ODEs are investigated and found to have different disruption capabilities against a model PLL. These differences are explored and implications for their use as input signal models are discussed. The PLL was chosen here as an archetypal example but the approach has broad applicability to any input/output system for which a desired input cannot be obtained analytically.
NASA Astrophysics Data System (ADS)
Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.
2015-12-01
This study contributes a diagnostic assessment of multiobjective evolutionary algorithm (MOEA) search on a set of water resources problem formulations with different configurations of constraints. Unlike constraints in classical optimization modeling, constraints within MOEA simulation-optimization represent limits on acceptable performance that delineate whether solutions within the search problem are feasible. Constraints are relevant because of the emergent pressures on water resources systems: increasing public awareness of their sustainability, coupled with regulatory pressures on water management agencies. In this study, we test several state-of-the-art MOEAs that utilize restricted tournament selection for constraint handling on varying configurations of water resources planning problems. For example, a problem that has no constraints on performance levels will be compared with a problem with several severe constraints, and a problem with constraints that have less severe values on the constraint thresholds. One such problem, Lower Rio Grande Valley (LRGV) portfolio planning, has been solved with a suite of constraints that ensure high reliability, low cost variability, and acceptable performance in a single year severe drought. But to date, it is unclear whether or not the constraints are negatively affecting MOEAs' ability to solve the problem effectively. Two categories of results are explored. The first category uses control maps of algorithm performance to determine if the algorithm's performance is sensitive to user-defined parameters. The second category uses run-time performance metrics to determine the time required for the algorithm to reach sufficient levels of convergence and diversity on the solution sets. Our work exploring the effect of constraints will better enable practitioners to define MOEA problem formulations for real-world systems, especially when stakeholders are concerned with achieving fixed levels of performance according to one or
Energy efficient model based algorithm for control of building HVAC systems.
Kirubakaran, V; Sahu, Chinmay; Radhakrishnan, T K; Sivakumaran, N
2015-11-01
Energy efficient designs are receiving increasing attention in various fields of engineering. Heating ventilation and air conditioning (HVAC) control system designs involve improved energy usage with an acceptable relaxation in thermal comfort. In this paper, real time data from a building HVAC system provided by BuildingLAB is considered. A resistor-capacitor (RC) framework for representing thermal dynamics of the building is estimated using particle swarm optimization (PSO) algorithm. With objective costs as thermal comfort (deviation of room temperature from required temperature) and energy measure (Ecm) explicit MPC design for this building model is executed based on its state space representation of the supply water temperature (input)/room temperature (output) dynamics. The controllers are subjected to servo tracking and external disturbance (ambient temperature) is provided from the real time data during closed loop control. The control strategies are ported on a PIC32mx series microcontroller platform. The building model is implemented in MATLAB and hardware in loop (HIL) testing of the strategies is executed over a USB port. Results indicate that compared to traditional proportional integral (PI) controllers, the explicit MPC's improve both energy efficiency and thermal comfort significantly.
Energy efficient model based algorithm for control of building HVAC systems.
Kirubakaran, V; Sahu, Chinmay; Radhakrishnan, T K; Sivakumaran, N
2015-11-01
Energy efficient designs are receiving increasing attention in various fields of engineering. Heating ventilation and air conditioning (HVAC) control system designs involve improved energy usage with an acceptable relaxation in thermal comfort. In this paper, real time data from a building HVAC system provided by BuildingLAB is considered. A resistor-capacitor (RC) framework for representing thermal dynamics of the building is estimated using particle swarm optimization (PSO) algorithm. With objective costs as thermal comfort (deviation of room temperature from required temperature) and energy measure (Ecm) explicit MPC design for this building model is executed based on its state space representation of the supply water temperature (input)/room temperature (output) dynamics. The controllers are subjected to servo tracking and external disturbance (ambient temperature) is provided from the real time data during closed loop control. The control strategies are ported on a PIC32mx series microcontroller platform. The building model is implemented in MATLAB and hardware in loop (HIL) testing of the strategies is executed over a USB port. Results indicate that compared to traditional proportional integral (PI) controllers, the explicit MPC's improve both energy efficiency and thermal comfort significantly. PMID:25869418
A simple model based magnet sorting algorithm for planar hybrid undulators
Rakowsky, G.
2010-05-23
Various magnet sorting strategies have been used to optimize undulator performance, ranging from intuitive pairing of high- and low-strength magnets, to full 3D FEM simulation with 3-axis Helmholtz coil magnet data. In the extreme, swapping magnets in a full field model to minimize trajectory wander and rms phase error can be time consuming. This paper presents a simpler approach, extending the field error signature concept to obtain trajectory displacement, kick angle and phase error signatures for each component of magnetization error from a Radia model of a short hybrid-PM undulator. We demonstrate that steering errors and phase errors are essentially decoupled and scalable from measured X, Y and Z components of magnetization. Then, for any given sequence of magnets, rms trajectory and phase errors are obtained from simple cumulative sums of the scaled displacements and phase errors. The cost function (a weighted sum of these errors) is then minimized by swapping magnets, using one's favorite optimization algorithm. This approach was applied recently at NSLS to a short in-vacuum undulator, which required no subsequent trajectory or phase shimming. Trajectory and phase signatures are also obtained for some mechanical errors, to guide 'virtual shimming' and specifying mechanical tolerances. Some simple inhomogeneities are modeled to assess their error contributions.
Assessment of Rainfall-Runoff Simulation Model Based on Satellite Algorithm
NASA Astrophysics Data System (ADS)
Nemati, A. R.; Zakeri Niri, M.; Moazami, S.
2015-12-01
Simulation of rainfall-runoff process is one of the most important research fields in hydrology and water resources. Generally, the models used in this section are divided into two conceptual and data-driven categories. In this study, a conceptual model and two data-driven models have been used to simulate rainfall-runoff process in Tamer sub-catchment located in Gorganroud watershed in Iran. The conceptual model used is HEC-HMS, and data-driven models are neural network model of multi-layer Perceptron (MLP) and support vector regression (SVR). In addition to simulation of rainfall-runoff process using the recorded land precipitation, the performance of four satellite algorithms of precipitation, that is, CMORPH, PERSIANN, TRMM 3B42 and TRMM 3B42RT were studied. In simulation of rainfall-runoff process, calibration and accuracy of the models were done based on satellite data. The results of the research based on three criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) showed that in this part the two models of SVR and MLP could perform the simulation of runoff in a relatively appropriate way, but in simulation of the maximum values of the flow, the error of models increased.
Bowen, J.; Dozier, G.
1996-12-31
This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves (Constraint Satisfaction Problems (CSPs)). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method, on a test suite of 750 randomly generated CSPs.
Combining evolutionary algorithms with oblique decision trees to detect bent double galaxies
Cantu-Paz, E; Kamath, C
2000-06-22
Decision trees have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis-parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learnt is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction with deterministic hill climbing and the use of simulated annealing. In this paper, they use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. They demonstrate the technique on a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology, and describe their experiences with several split evaluation criteria.
A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization
NASA Astrophysics Data System (ADS)
Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.
2015-08-01
A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.
NASA Astrophysics Data System (ADS)
Bonissone, Stefano R.; Subbu, Raj
2002-12-01
In multi-objective optimization (MOO) problems we need to optimize many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionary algorithms (EAs) to solve these problems. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.
Application of a multi-objective evolutionary algorithm to the spacecraft stationkeeping problem
NASA Astrophysics Data System (ADS)
Myers, Philip L.; Spencer, David B.
2016-10-01
Satellite operations are becoming an increasingly private industry, requiring increased profitability. Efficient and safe operation of satellites in orbit will ensure longer lasting and more profitable satellite services. This paper focuses on the use of a multi-objective evolutionary algorithm to schedule the maneuvers of a hypothetical satellite operating at geosynchronous altitude, by seeking to minimize the propellant consumed through the execution of stationkeeping maneuvers and the time the satellite is displaced from its desired orbital plane. Minimization of the time out of place increases the operational availability and minimizing the propellant usage which allows the spacecraft to operate longer. North-South stationkeeping was studied in this paper, through the use of a set of orbit inclination change maneuvers each year. Two cases for the maximum number of maneuvers to be executed were considered, with four and five maneuvers per year. The results delivered by the algorithm provide maneuver schedules which require 40-100 m/s of total Δv for two years of operation, with the satellite maintaining the satellite's orbital plane to within 0.1° between 84% and 96% of the two years being modeled.
NASA Astrophysics Data System (ADS)
Sarkar, K.; Topsakal, M.; Wentzcovitch, R. M.
2015-12-01
We attempt to achieve the accuracy of full-potential linearized augmented-plane-wave (FLAPW) method, as implemented in the WIEN2k code, at the favorable computational efficiency of the projector augmented wave (PAW) method for ab initio calculations of solids. For decades, PAW datasets have been generated by manually choosing its parameters and by visually inspecting its logarithmic derivatives, partial wave, and projector basis set. In addition to being tedious and error-prone, this procedure is inadequate because it is impractical to manually explore the full parameter space, as an infinite number of PAW parameter sets for a given augmentation radius can be generated maintaining all the constraints on logarithmic derivatives and basis sets. Performance verification of all plausible solutions against FLAPW is also impractical. Here we report the development of a hybrid algorithm to construct optimized PAW basis sets that can closely reproduce FLAPW results from zero to ultra-high pressures. The approach applies evolutionary computing (EC) to generate optimum PAW parameter sets using the ATOMPAW code. We have the Quantum ESPRESSO distribution to generate equation of state (EOS) to be compared with WIEN2k EOSs set as target. Softer PAW potentials reproducing yet more closely FLAPW EOSs can be found with this method. We demonstrate its working principles and workability by optimizing PAW basis functions for carbon, magnesium, aluminum, silicon, calcium, and iron atoms. The algorithm requires minimal user intervention in a sense that there is no requirement of visual inspection of logarithmic derivatives or of projector functions.
Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms
Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun
2011-01-01
This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927
NASA Astrophysics Data System (ADS)
Wang, J.; Cai, X.
2007-12-01
A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators
NASA Astrophysics Data System (ADS)
Manzano-Agugliaro, F.; San-Antonio-Gómez, C.; López, S.; Montoya, F. G.; Gil, C.
2013-08-01
When historical map data are compared with modern cartography, the old map coordinates must be transformed to the current system. However, historical data often exhibit heterogeneous quality. In calculating the transformation parameters between the historical and modern maps, it is often necessary to discard highly uncertain data. An optimal balance between the objectives of minimising the transformation error and eliminating as few points as possible can be achieved by generating a Pareto front of solutions using evolutionary genetic algorithms. The aim of this paper is to assess the performance of evolutionary algorithms in determining the accuracy of historical maps in regard to modern cartography. When applied to the 1787 Tomas Lopez map, the use of evolutionary algorithms reduces the linear error by 40% while eliminating only 2% of the data points. The main conclusion of this paper is that evolutionary algorithms provide a promising alternative for the transformation of historical map coordinates and determining the accuracy of historical maps in regard to modern cartography, particularly when the positional quality of the data points used cannot be assured.
A possibilistic approach to rotorcraft design through a multi-objective evolutionary algorithm
NASA Astrophysics Data System (ADS)
Chae, Han Gil
Most of the engineering design processes in use today in the field may be considered as a series of successive decision making steps. The decision maker uses information at hand, determines the direction of the procedure, and generates information for the next step and/or other decision makers. However, the information is often incomplete, especially in the early stages of the design process of a complex system. As the complexity of the system increases, uncertainties eventually become unmanageable using traditional tools. In such a case, the tools and analysis values need to be "softened" to account for the designer's intuition. One of the methods that deals with issues of intuition and incompleteness is possibility theory. Through the use of possibility theory coupled with fuzzy inference, the uncertainties estimated by the intuition of the designer are quantified for design problems. By involving quantified uncertainties in the tools, the solutions can represent a possible set, instead of a crisp spot, for predefined levels of certainty. From a different point of view, it is a well known fact that engineering design is a multi-objective problem or a set of such problems. The decision maker aims to find satisfactory solutions, sometimes compromising the objectives that conflict with each other. Once the candidates of possible solutions are generated, a satisfactory solution can be found by various decision-making techniques. A number of multi-objective evolutionary algorithms (MOEAs) have been developed, and can be found in the literature, which are capable of generating alternative solutions and evaluating multiple sets of solutions in one single execution of an algorithm. One of the MOEA techniques that has been proven to be very successful for this class of problems is the strength Pareto evolutionary algorithm (SPEA) which falls under the dominance-based category of methods. The Pareto dominance that is used in SPEA, however, is not enough to account for the
NASA Astrophysics Data System (ADS)
Asouti, V. G.; Giannakoglou, K. C.
2012-07-01
This article presents a solution method to the unit commitment problem with probabilistic unit failures and repairs, which is based on evolutionary algorithms and Monte Carlo simulations. Regarding the latter, thousands of availability-unavailability trial time patterns along the scheduling horizon are generated. The objective function to be minimised is the expected total operating cost, computed after adapting any candidate solution, i.e. any series of generating/non-generating (ON/OFF) unit states, to the availability-unavailability patterns and performing evaluations by considering fuel, start-up and shutdown costs as well as the cost for buying electricity from external resources, if necessary. The proposed method introduces a new efficient chromosome representation: the decision variables are integer IDs corresponding to the binary-to-decimal converted ON/OFF (1/0) scenarios that cover the demand in each hour. In contrast to previous methods using binary strings as chromosomes, the new chromosome must be penalised only if any of the constraints regarding start-up, shutdown and ramp times cannot be met, chromosome repair is avoided and, consequently, the dispatch problems are solved once in the preparatory phase instead of during the evolution. For all these reasons, with or without probabilistic outages, the proposed algorithm has much lower CPU cost. In addition, if probabilistic outages are taken into account, a hierarchical evaluation scheme offers extra noticeable gain in CPU cost: the population members are approximately pre-evaluated using a small 'representative' set of the Monte Carlo simulations and only a few top population members undergo evaluations through the full Monte Carlo simulations. The hierarchical scheme makes the proposed method about one order of magnitude faster than its conventional counterpart.
Wu, Guohua; Pedrycz, Witold; Li, Haifeng; Qiu, Dishan; Ma, Manhao; Liu, Jin
2013-01-01
Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge that, in an unconstrained and first-order derivative optimization function, the optimal solution locates in a local extreme point at which the partial derivative over each variable equals zero. Through this collective of partial derivative equations, some quantitative relations among different variables can be obtained. These variable relations have to be satisfied in the optimal solution. With the use of such relations, VRS could reduce the number of variables and shrink the solution space when using EAs to deal with the optimization function, thus improving the optimizing speed and quality. When we apply VRS to optimization problems, we just need to modify the calculation approach of the objective function. Therefore, practically, it can be integrated with any EA. In this study, VRS is combined with particle swarm optimization variants and tested on several benchmark optimization functions and a real-world optimization problem. Computational results and comparative study demonstrate the effectiveness of VRS. PMID:24250256
NASA Astrophysics Data System (ADS)
Sarjaš, Andrej; Chowdhury, Amor; Svečko, Rajko
2016-09-01
This paper presents the synthesis of an optimal robust controller design using the polynomial pole placement technique and multi-criteria optimisation procedure via an evolutionary computation algorithm - differential evolution. The main idea of the design is to provide a reliable fixed-order robust controller structure and an efficient closed-loop performance with a preselected nominally characteristic polynomial. The multi-criteria objective functions have quasi-convex properties that significantly improve convergence and the regularity of the optimal/sub-optimal solution. The fundamental aim of the proposed design is to optimise those quasi-convex functions with fixed closed-loop characteristic polynomials, the properties of which are unrelated and hard to present within formal algebraic frameworks. The objective functions are derived from different closed-loop criteria, such as robustness with metric ?∞, time performance indexes, controller structures, stability properties, etc. Finally, the design results from the example verify the efficiency of the controller design and also indicate broader possibilities for different optimisation criteria and control structures.
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.
Analysis of high resolution FTIR spectra from synchrotron sources using evolutionary algorithms
NASA Astrophysics Data System (ADS)
van Wijngaarden, Jennifer; Desmond, Durell; Leo Meerts, W.
2015-09-01
Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm-1 using synchrotron radiation from the Canadian Light Source from 500 to 560 cm-1. The in-plane ring deformation mode (ν13) at ∼529 cm-1 exhibits dense rotational structure due to the presence of ring inversion tunneling and leads to a doubling of all transitions. Preliminary analysis of the experimental spectrum was pursued via traditional methods involving assignment of quantum numbers to individual transitions in order to conduct least squares fitting to determine the spectroscopic parameters. Following this approach, the assignment of 2358 transitions led to the experimental determination of an effective Hamiltonian. This model describes transitions in the P and R branches to J‧ = 60 and Ka‧ = 10 that connect the tunneling split ground and vibrationally excited states of the ν13 band although a small number of low intensity features remained unassigned. The use of evolutionary algorithms (EA) for automated assignment was explored in tandem and yielded a set of spectroscopic constants that re-create this complex experimental spectrum to a similar degree. The EA routine was also applied to the previously well-understood ring puckering vibration of another four-membered ring, azetidine (Zaporozan et al., 2010). This test provided further evidence of the robust nature of the EA method when applied to spectra for which the underlying physics is well understood.
Pedrycz, Witold; Qiu, Dishan; Ma, Manhao; Liu, Jin
2013-01-01
Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge that, in an unconstrained and first-order derivative optimization function, the optimal solution locates in a local extreme point at which the partial derivative over each variable equals zero. Through this collective of partial derivative equations, some quantitative relations among different variables can be obtained. These variable relations have to be satisfied in the optimal solution. With the use of such relations, VRS could reduce the number of variables and shrink the solution space when using EAs to deal with the optimization function, thus improving the optimizing speed and quality. When we apply VRS to optimization problems, we just need to modify the calculation approach of the objective function. Therefore, practically, it can be integrated with any EA. In this study, VRS is combined with particle swarm optimization variants and tested on several benchmark optimization functions and a real-world optimization problem. Computational results and comparative study demonstrate the effectiveness of VRS. PMID:24250256
Enhancements of evolutionary algorithm for the complex requirements of a nurse scheduling problem
NASA Astrophysics Data System (ADS)
Tein, Lim Huai; Ramli, Razamin
2014-12-01
Over the years, nurse scheduling is a noticeable problem that is affected by the global nurse turnover crisis. The more nurses are unsatisfied with their working environment the more severe the condition or implication they tend to leave. Therefore, the current undesirable work schedule is partly due to that working condition. Basically, there is a lack of complimentary requirement between the head nurse's liability and the nurses' need. In particular, subject to highly nurse preferences issue, the sophisticated challenge of doing nurse scheduling is failure to stimulate tolerance behavior between both parties during shifts assignment in real working scenarios. Inevitably, the flexibility in shifts assignment is hard to achieve for the sake of satisfying nurse diverse requests with upholding imperative nurse ward coverage. Hence, Evolutionary Algorithm (EA) is proposed to cater for this complexity in a nurse scheduling problem (NSP). The restriction of EA is discussed and thus, enhancement on the EA operators is suggested so that the EA would have the characteristic of a flexible search. This paper consists of three types of constraints which are the hard, semi-hard and soft constraints that can be handled by the EA with enhanced parent selection and specialized mutation operators. These operators and EA as a whole contribute to the efficiency of constraint handling, fitness computation as well as flexibility in the search, which correspond to the employment of exploration and exploitation principles.
NASA Astrophysics Data System (ADS)
Ong, Zhiyang; Lo, Andy Hao-Wei; Berryman, Matthew; Abbott, Derek
2005-12-01
The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.
NASA Astrophysics Data System (ADS)
Smith, R.; Kasprzyk, J. R.; Zagona, E. A.
2013-12-01
Population growth and climate change, combined with difficulties in building new infrastructure, motivate portfolio-based solutions to ensuring sufficient water supply. Powerful simulation models with graphical user interfaces (GUI) are often used to evaluate infrastructure portfolios; these GUI based models require manual modification of the system parameters, such as reservoir operation rules, water transfer schemes, or system capacities. Multiobjective evolutionary algorithm (MOEA) based optimization can be employed to balance multiple objectives and automatically suggest designs for infrastructure systems, but MOEA based decision support typically uses a fixed problem formulation (i.e., a single set of objectives, decisions, and constraints). This presentation suggests a dynamic framework for linking GUI-based infrastructure models with MOEA search. The framework begins with an initial formulation which is solved using a MOEA. Then, stakeholders can interact with candidate solutions, viewing their properties in the GUI model. This is followed by changes in the formulation which represent users' evolving understanding of exigent system properties. Our case study is built using RiverWare, an object-oriented, data-centered model that facilitates the representation of a diverse array of water resources systems. Results suggest that assumptions within the initial MOEA search are violated after investigating tradeoffs and reveal how formulations should be modified to better capture stakeholders' preferences.
NASA Astrophysics Data System (ADS)
Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.
2016-04-01
Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the
BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing.
Kao, Wei-Chun; Stevens, Kristian; Song, Yun S
2009-10-01
Extracting sequence information from raw images of fluorescence is the foundation underlying several high-throughput sequencing platforms. Some of the main challenges associated with this technology include reducing the error rate, assigning accurate base-specific quality scores, and reducing the cost of sequencing by increasing the throughput per run. To demonstrate how computational advancement can help to meet these challenges, a novel model-based base-calling algorithm, BayesCall, is introduced for the Illumina sequencing platform. Being founded on the tools of statistical learning, BayesCall is flexible enough to incorporate various features of the sequencing process. In particular, it can easily incorporate time-dependent parameters and model residual effects. This new approach significantly improves the accuracy over Illumina's base-caller Bustard, particularly in the later cycles of a sequencing run. For 76-cycle data on a standard viral sample, phiX174, BayesCall improves Bustard's average per-base error rate by approximately 51%. The probability of observing each base can be readily computed in BayesCall, and this probability can be transformed into a useful base-specific quality score with a high discrimination ability. A detailed study of BayesCall's performance is presented here. PMID:19661376
NASA Astrophysics Data System (ADS)
Karakostas, Spiros
2015-05-01
The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.
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.
Holmes, Tim; Zanker, Johannes M.
2013-01-01
Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioral measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been demonstrated as a tool to identify aesthetic preferences (Holmes and Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of color and shape which have been promoted in the Bauhaus arts school. We used the same three shapes (square, circle, triangle) used by Kandinsky (1923), with the three color palette from the original experiment (A), an extended seven color palette (B), and eight different shape orientation (C). Participants were instructed to look for their preferred circle, triangle or square in displays with eight stimuli of different shapes, colors and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested six participants extensively on the different conditions and found consistent preferences for color-shape combinations for individuals, but little evidence at the group level for clear color/shape preference consistent with Kandinsky's claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of color and shapes, but also that these associations are robust within a single individual. These individual differences go some way toward challenging the claims of the universal preference for color/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the
Holmes, Tim; Zanker, Johannes M
2013-01-01
Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioral measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been demonstrated as a tool to identify aesthetic preferences (Holmes and Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of color and shape which have been promoted in the Bauhaus arts school. We used the same three shapes (square, circle, triangle) used by Kandinsky (1923), with the three color palette from the original experiment (A), an extended seven color palette (B), and eight different shape orientation (C). Participants were instructed to look for their preferred circle, triangle or square in displays with eight stimuli of different shapes, colors and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested six participants extensively on the different conditions and found consistent preferences for color-shape combinations for individuals, but little evidence at the group level for clear color/shape preference consistent with Kandinsky's claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of color and shapes, but also that these associations are robust within a single individual. These individual differences go some way toward challenging the claims of the universal preference for color/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the
NASA Astrophysics Data System (ADS)
Ketabchi, Hamed; Ataie-Ashtiani, Behzad
2015-01-01
This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision
Dunet, Vincent; Hachulla, Anne-Lise; Grimm, Jochen; Beigelman-Aubry, Catherine
2016-01-01
Background Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. Purpose To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. Material and Methods Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. Results The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. Conclusion The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.
Dunet, Vincent; Hachulla, Anne-Lise; Grimm, Jochen; Beigelman-Aubry, Catherine
2016-01-01
Background Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. Purpose To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. Material and Methods Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. Results The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. Conclusion The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique. PMID:27635253
NASA Astrophysics Data System (ADS)
Ma, Zhanshan (Sam)
Competition, cooperation and communication are the three fundamental relationships upon which natural selection acts in the evolution of life. Evolutionary game theory (EGT) is a 'marriage' between game theory and Darwin's evolution theory; it gains additional modeling power and flexibility by adopting population dynamics theory. In EGT, natural selection acts as optimization agents and produces inherent strategies, which eliminates some essential assumptions in traditional game theory such as rationality and allows more realistic modeling of many problems. Prisoner's Dilemma (PD) and Sir Philip Sidney (SPS) games are two well-known examples of EGT, which are formulated to study cooperation and communication, respectively. Despite its huge success, EGT exposes a certain degree of weakness in dealing with time-, space- and covariate-dependent (i.e., dynamic) uncertainty, vulnerability and deception. In this paper, I propose to extend EGT in two ways to overcome the weakness. First, I introduce survival analysis modeling to describe the lifetime or fitness of game players. This extension allows more flexible and powerful modeling of the dynamic uncertainty and vulnerability (collectively equivalent to the dynamic frailty in survival analysis). Secondly, I introduce agreement algorithms, which can be the Agreement algorithms in distributed computing (e.g., Byzantine Generals Problem [6][8], Dynamic Hybrid Fault Models [12]) or any algorithms that set and enforce the rules for players to determine their consensus. The second extension is particularly useful for modeling dynamic deception (e.g., asymmetric faults in fault tolerance and deception in animal communication). From a computational perspective, the extended evolutionary game theory (EEGT) modeling, when implemented in simulation, is equivalent to an optimization methodology that is similar to evolutionary computing approaches such as Genetic algorithms with dynamic populations [15][17].
Yi, Jianbing; Yang, Xuan Li, Yan-Ran; Chen, Guoliang
2015-10-15
Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the
NASA Astrophysics Data System (ADS)
Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.
2016-03-01
Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.
NASA Astrophysics Data System (ADS)
Ramli, Razamin; Tein, Lim Huai
2016-08-01
A good work schedule can improve hospital operations by providing better coverage with appropriate staffing levels in managing nurse personnel. Hence, constructing the best nurse work schedule is the appropriate effort. In doing so, an improved selection operator in the Evolutionary Algorithm (EA) strategy for a nurse scheduling problem (NSP) is proposed. The smart and efficient scheduling procedures were considered. Computation of the performance of each potential solution or schedule was done through fitness evaluation. The best so far solution was obtained via special Maximax&Maximin (MM) parent selection operator embedded in the EA, which fulfilled all constraints considered in the NSP.
NASA Astrophysics Data System (ADS)
Cody, B. M.; Gonzalez-Nicolas, A.; Bau, D. A.
2011-12-01
Carbon capture and storage (CCS) has been proposed as a method of reducing global carbon dioxide (CO2) emissions. Although CCS has the potential to greatly retard greenhouse gas loading to the atmosphere while cleaner, more sustainable energy solutions are developed, there is a possibility that sequestered CO2 may leak and intrude into and adversely affect groundwater resources. It has been reported [1] that, while CO2 intrusion typically does not directly threaten underground drinking water resources, it may cause secondary effects, such as the mobilization of hazardous inorganic constituents present in aquifer minerals and changes in pH values. These risks must be fully understood and minimized before CCS project implementation. Combined management of project resources and leakage risk is crucial for the implementation of CCS. In this work, we present a method of: (a) minimizing the total CCS cost, the summation of major project costs with the cost associated with CO2 leakage; and (b) maximizing the mass of injected CO2, for a given proposed sequestration site. Optimization decision variables include the number of CO2 injection wells, injection rates, and injection well locations. The capital and operational costs of injection wells are directly related to injection well depth, location, injection flow rate, and injection duration. The cost of leakage is directly related to the mass of CO2 leaked through weak areas, such as abandoned oil wells, in the cap rock layers overlying the injected formation. Additional constraints on fluid overpressure caused by CO2 injection are imposed to maintain predefined effective stress levels that prevent cap rock fracturing. Here, both mass leakage and fluid overpressure are estimated using two semi-analytical models based upon work by [2,3]. A multi-objective evolutionary algorithm coupled with these semi-analytical leakage flow models is used to determine Pareto-optimal trade-off sets giving minimum total cost vs. maximum mass
NASA Astrophysics Data System (ADS)
Marghany, M.
2015-06-01
Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.
An evolutionary algorithm for global optimization based on self-organizing maps
NASA Astrophysics Data System (ADS)
Barmada, Sami; Raugi, Marco; Tucci, Mauro
2016-10-01
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data
Bywater, Robert P.
2016-01-01
Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before. PMID:26963911
Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data.
Bywater, Robert P
2016-01-01
Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before.
NASA Technical Reports Server (NTRS)
Xiang, Xuwu; Smith, Eric A.; Tripoli, Gregory J.
1992-01-01
A hybrid statistical-physical retrieval scheme is explored which combines a statistical approach with an approach based on the development of cloud-radiation models designed to simulate precipitating atmospheres. The algorithm employs the detailed microphysical information from a cloud model as input to a radiative transfer model which generates a cloud-radiation model database. Statistical procedures are then invoked to objectively generate an initial guess composite profile data set from the database. The retrieval algorithm has been tested for a tropical typhoon case using Special Sensor Microwave/Imager (SSM/I) data and has shown satisfactory results.
Zhou, Jianzhong; Song, Lixiang; Kursan, Suncana; Liu, Yi
2015-05-01
A two-dimensional coupled water quality model is developed for modeling the flow-mass transport in shallow water. To simulate shallow flows on complex topography with wetting and drying, an unstructured grid, well-balanced, finite volume algorithm is proposed for numerical resolution of a modified formulation of two-dimensional shallow water equations. The slope-limited linear reconstruction method is used to achieve second-order accuracy in space. The algorithm adopts a HLLC-based integrated solver to compute the flow and mass transport fluxes simultaneously, and uses Hancock's predictor-corrector scheme for efficient time stepping as well as second-order temporal accuracy. The continuity and momentum equations are updated in both wet and dry cells. A new hybrid method, which can preserve the well-balanced property of the algorithm for simulations involving flooding and recession, is proposed for bed slope terms approximation. The effectiveness and robustness of the proposed algorithm are validated by the reasonable good agreement between numerical and reference results of several benchmark test cases. Results show that the proposed coupled flow-mass transport model can simulate complex flows and mass transport in shallow water.
Zhou, Jianzhong; Song, Lixiang; Kursan, Suncana; Liu, Yi
2015-05-01
A two-dimensional coupled water quality model is developed for modeling the flow-mass transport in shallow water. To simulate shallow flows on complex topography with wetting and drying, an unstructured grid, well-balanced, finite volume algorithm is proposed for numerical resolution of a modified formulation of two-dimensional shallow water equations. The slope-limited linear reconstruction method is used to achieve second-order accuracy in space. The algorithm adopts a HLLC-based integrated solver to compute the flow and mass transport fluxes simultaneously, and uses Hancock's predictor-corrector scheme for efficient time stepping as well as second-order temporal accuracy. The continuity and momentum equations are updated in both wet and dry cells. A new hybrid method, which can preserve the well-balanced property of the algorithm for simulations involving flooding and recession, is proposed for bed slope terms approximation. The effectiveness and robustness of the proposed algorithm are validated by the reasonable good agreement between numerical and reference results of several benchmark test cases. Results show that the proposed coupled flow-mass transport model can simulate complex flows and mass transport in shallow water. PMID:25686488
Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu
2013-01-01
Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .
NASA Astrophysics Data System (ADS)
Wu, J.; Yang, Y.; Luo, Q.; Wu, J.
2012-12-01
This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.
Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao
2015-01-01
Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly
Bator, Marcin; Nieniewski, Mariusz
2012-02-01
Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF) = 0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF = 0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI) = 2.
The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules.
Lameijer, Eric-Wubbo; Kok, Joost N; Bäck, Thomas; Ijzerman, Ad P
2006-01-01
We developed a software tool to design drug-like molecules, the "Molecule Evoluator", which we introduce and describe here. An atom-based evolutionary approach was used allowing both several types of mutation and crossover to occur. The novelty, we claim, is the unprecedented interactive evolution, in which the user acts as a fitness function. This brings a human being's creativity, implicit knowledge, and imagination into the design process, next to the more standard chemical rules. Proof-of-concept was demonstrated in a number of ways, both computationally and in the lab. Thus, we synthesized a number of compounds designed with the aid of the Molecule Evoluator. One of these is described here, a new chemical entity with activity on alpha-adrenergic receptors.
NASA Astrophysics Data System (ADS)
Knapmeyer-Endrun, B.; Hammer, C.
2014-12-01
The seismometers that the Apollo astronauts deployed on the Moon provide the only recordings of seismic events from any extra-terrestrial body so far. These lunar events are significantly different from ones recorded on Earth, in terms of both signal shape and source processes. Thus they are a valuable test case for any experiment in planetary seismology. In this study, we analyze Apollo 16 data with a single-station event detection and classification algorithm in view of NASA's upcoming InSight mission to Mars. InSight, scheduled for launch in early 2016, has the goal to investigate Mars' internal structure by deploying a seismometer on its surface. As the mission does not feature any orbiter, continuous data will be relayed to Earth at a reduced rate. Full range data will only be available by requesting specific time-windows within a few days after the receipt of the original transmission. We apply a recently introduced algorithm based on hidden Markov models that requires only a single example waveform of each event class for training appropriate models. After constructing the prototypes we detect and classify impacts and deep and shallow moonquakes. Initial results for 1972 (year of station installation with 8 months of data) indicate a high detection rate of over 95% for impacts, of which more than 80% are classified correctly. Deep moonquakes, which occur in large amounts, but often show only very weak signals, are detected with less certainty (~70%). As there is only one weak shallow moonquake covered, results for this event class are not statistically significant. Daily adjustments of the background noise model help to reduce false alarms, which are mainly erroneous deep moonquake detections, by about 25%. The algorithm enables us to classify events that were previously listed in the catalog without classification, and, through the combined use of long period and short period data, identify some unlisted local impacts as well as at least two yet unreported
NASA Astrophysics Data System (ADS)
Liu, Jiulong; Zhang, Xue; Zhang, Xiaoqun; Zhao, Hongkai; Gao, Yu; Thomas, David; Low, Daniel A.; Gao, Hao
2015-11-01
4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.
Constructing large-scale genetic maps using an evolutionary strategy algorithm.
Mester, D; Ronin, Y; Minkov, D; Nevo, E; Korol, A
2003-01-01
This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with approximately 50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology. PMID:14704202
NASA Astrophysics Data System (ADS)
Amian, M.; Setarehdan, S. Kamaledin; Yousefi, H.
2014-09-01
Functional Near infrared spectroscopy (fNIRS) is a newly noninvasive way to measure oxy hemoglobin and deoxy hemoglobin concentration changes of human brain. Relatively safe and affordable than other functional imaging techniques such as fMRI, it is widely used for some special applications such as infant examinations and pilot's brain monitoring. In such applications, fNIRS data sometimes suffer from undesirable movements of subject's head which called motion artifact and lead to a signal corruption. Motion artifact in fNIRS data may result in fallacy of concluding or diagnosis. In this work we try to reduce these artifacts by a novel Kalman filtering algorithm that is based on an autoregressive moving average (ARMA) model for fNIRS system. Our proposed method does not require to any additional hardware and sensor and also it does not need to whole data together that once were of ineluctable necessities in older algorithms such as adaptive filter and Wiener filtering. Results show that our approach is successful in cleaning contaminated fNIRS data.
Osaba, E.; Carballedo, R.; Diaz, F.; Onieva, E.; de la Iglesia, I.; Perallos, A.
2014-01-01
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. PMID:25165731
Hsieh, PingHsun; Woerner, August E.; Wall, Jeffrey D.; Lachance, Joseph; Tishkoff, Sarah A.; Gutenkunst, Ryan N.; Hammer, Michael F.
2016-01-01
Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr. PMID:26888264
Hsieh, PingHsun; Woerner, August E; Wall, Jeffrey D; Lachance, Joseph; Tishkoff, Sarah A; Gutenkunst, Ryan N; Hammer, Michael F
2016-03-01
Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr. PMID:26888264
On the Effects of Migration on the Fitness Distribution of Parallel Evolutionary Algorithms
Cantu-Paz, E.
2000-04-25
Migration of individuals between populations may increase the selection pressure. This has the desirable consequence of speeding up convergence, but it may result in an excessively rapid loss of variation that may cause the search to fail. This paper investigates the effects of migration on the distribution of fitness. It considers arbitrary migration rates and topologies with different number of neighbors, and it compares algorithms that are configured to have the same selection intensity. The results suggest that migration preserves more diversity as the number of neighbors of a deme increases.
NASA Astrophysics Data System (ADS)
Tang, Shihua; Li, Feida; Liu, Yintao; Lan, Lan; Zhou, Conglin; Huang, Qing
2015-12-01
With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods'. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.
NASA Astrophysics Data System (ADS)
Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.
2014-12-01
As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a
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.
NASA Astrophysics Data System (ADS)
Arnaout, A.; Fruhwirth, R.; Winter, M.; Esmael, B.; Thonhauser, G.
2012-04-01
The use of neural networks and advanced machine learning techniques in the oil & gas industry is a growing trend in the market. Especially in drilling oil & gas wells, prediction and monitoring different drilling parameters is an essential task to prevent serious problems like "Kick", "Lost Circulation" or "Stuck Pipe" among others. The hookload represents the weight load of the drill string at the crane hook. It is one of the most important parameters. During drilling the parameter "Weight on Bit" is controlled by the driller whereby the hookload is the only measure to monitor how much weight on bit is applied to the bit to generate the hole. Any changes in weight on bit will be directly reflected at the hookload. Furthermore any unwanted contact between the drill string and the wellbore - potentially leading to stuck pipe problem - will appear directly in the measurements of the hookload. Therefore comparison of the measured to the predicted hookload will not only give a clear idea on what is happening down-hole, it also enables the prediction of a number of important events that may cause problems in the borehole and yield in some - fortunately rare - cases in catastrophes like blow-outs. Heuristic models using highly sophisticated neural networks were designed for the hookload prediction; the training data sets were prepared in cooperation with drilling experts. Sensor measurements as well as a set of derived feature channels were used as input to the models. The contents of the final data set can be separated into (1) features based on rig operation states, (2) real-time sensors features and (3) features based on physics. A combination of novel neural network architecture - the Completely Connected Perceptron and parallel learning techniques which avoid trapping into local error minima - was used for building the models. In addition automatic network growing algorithms and highly sophisticated stopping criterions offer robust and efficient estimation of the
Wiese, Kay C; Hendriks, Andrew; Deschênes, Alain; Ben Youssef, Belgacem
2005-09-01
This paper presents a fully parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented on a 128-node Beowulf cluster. The PRNG comparison tests were performed with known structures whose sequences are 118, 122, 468, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures. Results indicate that P-RnaPredict demonstrated good prediction accuracy, particularly so for shorter sequences.
2013-01-01
Background Proteins are essential biological molecules which play vital roles in nearly all biological processes. It is the tertiary structure of a protein that determines its functions. Therefore the prediction of a protein's tertiary structure based on its primary amino acid sequence has long been the most important and challenging subject in biochemistry, molecular biology and biophysics. In the past, the HP lattice model was one of the ab initio methods that many researchers used to forecast the protein structure. Although these kinds of simplified methods could not achieve high resolution, they provided a macrocosm-optimized protein structure. The model has been employed to investigate general principles of protein folding, and plays an important role in the prediction of protein structures. Methods In this paper, we present an improved evolutionary algorithm for the protein folding problem. We study the problem on the 3D FCC lattice HP model which has been widely used in previous research. Our focus is to develop evolutionary algorithms (EA) which are robust, easy to implement and can handle various energy functions. We propose to combine three different local search methods, including lattice rotation for crossover, K-site move for mutation, and generalized pull move; these form our key components to improve previous EA-based approaches. Results We have carried out experiments over several data sets which were used in previous research. The results of the experiments show that our approach is able to find optimal conformations which were not found by previous EA-based approaches. Conclusions We have investigated the geometric properties of the 3D FCC lattice and developed several local search techniques to improve traditional EA-based approaches to the protein folding problem. It is known that EA-based approaches are robust and can handle arbitrary energy functions. Our results further show that by extensive development of local searches, EA can also be very
Beyer, Hans-Georg
2014-01-01
The convergence behaviors of so-called natural evolution strategies (NES) and of the information-geometric optimization (IGO) approach are considered. After a review of the NES/IGO ideas, which are based on information geometry, the implications of this philosophy w.r.t. optimization dynamics are investigated considering the optimization performance on the class of positive quadratic objective functions (the ellipsoid model). Exact differential equations describing the approach to the optimizer are derived and solved. It is rigorously shown that the original NES philosophy optimizing the expected value of the objective functions leads to very slow (i.e., sublinear) convergence toward the optimizer. This is the real reason why state of the art implementations of IGO algorithms optimize the expected value of transformed objective functions, for example, by utility functions based on ranking. It is shown that these utility functions are localized fitness functions that change during the IGO flow. The governing differential equations describing this flow are derived. In the case of convergence, the solutions to these equations exhibit an exponentially fast approach to the optimizer (i.e., linear convergence order). Furthermore, it is proven that the IGO philosophy leads to an adaptation of the covariance matrix that equals in the asymptotic limit-up to a scalar factor-the inverse of the Hessian of the objective function considered.
Beyer, Hans-Georg
2014-01-01
The convergence behaviors of so-called natural evolution strategies (NES) and of the information-geometric optimization (IGO) approach are considered. After a review of the NES/IGO ideas, which are based on information geometry, the implications of this philosophy w.r.t. optimization dynamics are investigated considering the optimization performance on the class of positive quadratic objective functions (the ellipsoid model). Exact differential equations describing the approach to the optimizer are derived and solved. It is rigorously shown that the original NES philosophy optimizing the expected value of the objective functions leads to very slow (i.e., sublinear) convergence toward the optimizer. This is the real reason why state of the art implementations of IGO algorithms optimize the expected value of transformed objective functions, for example, by utility functions based on ranking. It is shown that these utility functions are localized fitness functions that change during the IGO flow. The governing differential equations describing this flow are derived. In the case of convergence, the solutions to these equations exhibit an exponentially fast approach to the optimizer (i.e., linear convergence order). Furthermore, it is proven that the IGO philosophy leads to an adaptation of the covariance matrix that equals in the asymptotic limit-up to a scalar factor-the inverse of the Hessian of the objective function considered. PMID:24922548
NASA Astrophysics Data System (ADS)
An, Zhao; Zhounian, Lai; Peng, Wu; Linlin, Cao; Dazhuan, Wu
2016-07-01
This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.
Venkatakrishnan, S V; Drummy, Lawrence F; Jackson, Michael A; De Graef, Marc; Simmons, Jeff; Bouman, Charles A
2013-11-01
High angle annular dark field (HAADF)-scanning transmission electron microscope (STEM) data is increasingly being used in the physical sciences to research materials in 3D because it reduces the effects of Bragg diffraction seen in bright field TEM data. Typically, tomographic reconstructions are performed by directly applying either filtered back projection (FBP) or the simultaneous iterative reconstruction technique (SIRT) to the data. Since HAADF-STEM tomography is a limited angle tomography modality with low signal to noise ratio, these methods can result in significant artifacts in the reconstructed volume. In this paper, we develop a model based iterative reconstruction algorithm for HAADF-STEM tomography. We combine a model for image formation in HAADF-STEM tomography along with a prior model to formulate the tomographic reconstruction as a maximum a posteriori probability (MAP) estimation problem. Our formulation also accounts for certain missing measurements by treating them as nuisance parameters in the MAP estimation framework. We adapt the iterative coordinate descent algorithm to develop an efficient method to minimize the corresponding MAP cost function. Reconstructions of simulated as well as experimental data sets show results that are superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast. PMID:23955748
NASA Astrophysics Data System (ADS)
Just, Gabriel M. P.; Rupper, Patrick; Miller, Terry A.; Meerts, W. Leo
2009-06-01
Alkyl peroxy radicals long have been well known to bekey intermediates in atmospheric chemistry as well as in low temperature combustion. For the last several years, our group has generated a data set for these radicals using room temperature cavity ringdown spectroscopy. We have recently extended our investigations of these radicals to obtain a similar data set of spectra under jet cooled conditions using a quasi-Fourier-transform-limited laser source, a supersonic slit jet expansion, and a discharge. We were able to observe partially rotationally resolved spectra of isomers and conformers of several peroxy radicals such as methyl peroxy, CH_3O_2/CD_3O_2, ethyl peroxy, C_2H_5O_2 and C_2D_5O_2, propyl peroxy, C_3H_7O_2, and phenyl peroxy, C_6H_5O_2. To analyze our results we employed a new approach by using the evolutionary algorithm method, whereby we can effectively use both the frequency and the intensity information contained in the experimental spectra. This presentation will focus on the results from our fitted spectra which were obtained using this semi-automated method and will demonstrate the power of our technique .
NASA Astrophysics Data System (ADS)
Smith, R.; Kasprzyk, J. R.; Zagona, E. A.
2015-12-01
Instead of building new infrastructure to increase their supply reliability, water resource managers are often tasked with better management of current systems. The managers often have existing simulation models that aid their planning, and lack methods for efficiently generating and evaluating planning alternatives. This presentation discusses how multiobjective evolutionary algorithm (MOEA) decision support can be used with the sophisticated water infrastructure model, RiverWare, in highly constrained water planning environments. We first discuss a study that performed a many-objective tradeoff analysis of water supply in the Tarrant Regional Water District (TRWD) in Texas. RiverWare is combined with the Borg MOEA to solve a seven objective problem that includes systemwide performance objectives and individual reservoir storage reliability. Decisions within the formulation balance supply in multiple reservoirs and control pumping between the eastern and western parts of the system. The RiverWare simulation model is forced by two stochastic hydrology scenarios to inform how management changes in wet versus dry conditions. The second part of the presentation suggests how a broader set of RiverWare-MOEA studies can inform tradeoffs in other systems, especially in political situations where multiple actors are in conflict over finite water resources. By incorporating quantitative representations of diverse parties' objectives during the search for solutions, MOEAs may provide support for negotiations and lead to more widely beneficial water management outcomes.
Guerra, J G; Rubiano, J G; Winter, G; Guerra, A G; Alonso, H; Arnedo, M A; Tejera, A; Gil, J M; Rodríguez, R; Martel, P; Bolivar, J P
2015-11-01
The determination in a sample of the activity concentration of a specific radionuclide by gamma spectrometry needs to know the full energy peak efficiency (FEPE) for the energy of interest. The difficulties related to the experimental calibration make it advisable to have alternative methods for FEPE determination, such as the simulation of the transport of photons in the crystal by the Monte Carlo method, which requires an accurate knowledge of the characteristics and geometry of the detector. The characterization process is mainly carried out by Canberra Industries Inc. using proprietary techniques and methodologies developed by that company. It is a costly procedure (due to shipping and to the cost of the process itself) and for some research laboratories an alternative in situ procedure can be very useful. The main goal of this paper is to find an alternative to this costly characterization process, by establishing a method for optimizing the parameters of characterizing the detector, through a computational procedure which could be reproduced at a standard research lab. This method consists in the determination of the detector geometric parameters by using Monte Carlo simulation in parallel with an optimization process, based on evolutionary algorithms, starting from a set of reference FEPEs determined experimentally or computationally. The proposed method has proven to be effective and simple to implement. It provides a set of characterization parameters which it has been successfully validated for different source-detector geometries, and also for a wide range of environmental samples and certified materials.
Guerra, J G; Rubiano, J G; Winter, G; Guerra, A G; Alonso, H; Arnedo, M A; Tejera, A; Gil, J M; Rodríguez, R; Martel, P; Bolivar, J P
2015-11-01
The determination in a sample of the activity concentration of a specific radionuclide by gamma spectrometry needs to know the full energy peak efficiency (FEPE) for the energy of interest. The difficulties related to the experimental calibration make it advisable to have alternative methods for FEPE determination, such as the simulation of the transport of photons in the crystal by the Monte Carlo method, which requires an accurate knowledge of the characteristics and geometry of the detector. The characterization process is mainly carried out by Canberra Industries Inc. using proprietary techniques and methodologies developed by that company. It is a costly procedure (due to shipping and to the cost of the process itself) and for some research laboratories an alternative in situ procedure can be very useful. The main goal of this paper is to find an alternative to this costly characterization process, by establishing a method for optimizing the parameters of characterizing the detector, through a computational procedure which could be reproduced at a standard research lab. This method consists in the determination of the detector geometric parameters by using Monte Carlo simulation in parallel with an optimization process, based on evolutionary algorithms, starting from a set of reference FEPEs determined experimentally or computationally. The proposed method has proven to be effective and simple to implement. It provides a set of characterization parameters which it has been successfully validated for different source-detector geometries, and also for a wide range of environmental samples and certified materials. PMID:26188622
NASA Astrophysics Data System (ADS)
Wang, Lilie; Ding, George X.
2014-07-01
The out-of-field dose can be clinically important as it relates to the dose of the organ-at-risk, although the accuracy of its calculation in commercial radiotherapy treatment planning systems (TPSs) receives less attention. This study evaluates the uncertainties of out-of-field dose calculated with a model based dose calculation algorithm, anisotropic analytical algorithm (AAA), implemented in a commercial radiotherapy TPS, Varian Eclipse V10, by using Monte Carlo (MC) simulations, in which the entire accelerator head is modeled including the multi-leaf collimators. The MC calculated out-of-field doses were validated by experimental measurements. The dose calculations were performed in a water phantom as well as CT based patient geometries and both static and highly modulated intensity-modulated radiation therapy (IMRT) fields were evaluated. We compared the calculated out-of-field doses, defined as lower than 5% of the prescription dose, in four H&N cancer patients and two lung cancer patients treated with volumetric modulated arc therapy (VMAT) and IMRT techniques. The results show that the discrepancy of calculated out-of-field dose profiles between AAA and the MC depends on the depth and is generally less than 1% for in water phantom comparisons and in CT based patient dose calculations for static field and IMRT. In cases of VMAT plans, the difference between AAA and MC is <0.5%. The clinical impact resulting from the error on the calculated organ doses were analyzed by using dose-volume histograms. Although the AAA algorithm significantly underestimated the out-of-field doses, the clinical impact on the calculated organ doses in out-of-field regions may not be significant in practice due to very low out-of-field doses relative to the target dose.
NASA Astrophysics Data System (ADS)
Kotegawa, Tatsuya
Complexity in the Air Transportation System (ATS) arises from the intermingling of many independent physical resources, operational paradigms, and stakeholder interests, as well as the dynamic variation of these interactions over time. Currently, trade-offs and cost benefit analyses of new ATS concepts are carried out on system-wide evaluation simulations driven by air traffic forecasts that assume fixed airline routes. However, this does not well reflect reality as airlines regularly add and remove routes. A airline service route network evolution model that projects route addition and removal was created and combined with state-of-the-art air traffic forecast methods to better reflect the dynamic properties of the ATS in system-wide simulations. Guided by a system-of-systems framework, network theory metrics and machine learning algorithms were applied to develop the route network evolution models based on patterns extracted from historical data. Constructing the route addition section of the model posed the greatest challenge due to the large pool of new link candidates compared to the actual number of routes historically added to the network. Of the models explored, algorithms based on logistic regression, random forests, and support vector machines showed best route addition and removal forecast accuracies at approximately 20% and 40%, respectively, when validated with historical data. The combination of network evolution models and a system-wide evaluation tool quantified the impact of airline route network evolution on air traffic delay. The expected delay minutes when considering network evolution increased approximately 5% for a forecasted schedule on 3/19/2020. Performance trade-off studies between several airline route network topologies from the perspectives of passenger travel efficiency, fuel burn, and robustness were also conducted to provide bounds that could serve as targets for ATS transformation efforts. The series of analysis revealed that high
NASA Astrophysics Data System (ADS)
Schumann, A.; Priegnitz, M.; Schoene, S.; Enghardt, W.; Rohling, H.; Fiedler, F.
2016-10-01
Range verification and dose monitoring in proton therapy is considered as highly desirable. Different methods have been developed worldwide, like particle therapy positron emission tomography (PT-PET) and prompt gamma imaging (PGI). In general, these methods allow for a verification of the proton range. However, quantification of the dose from these measurements remains challenging. For the first time, we present an approach for estimating the dose from prompt γ-ray emission profiles. It combines a filtering procedure based on Gaussian-powerlaw convolution with an evolutionary algorithm. By means of convolving depth dose profiles with an appropriate filter kernel, prompt γ-ray depth profiles are obtained. In order to reverse this step, the evolutionary algorithm is applied. The feasibility of this approach is demonstrated for a spread-out Bragg-peak in a water target.
Clausen, Rudy; Ma, Buyong; Nussinov, Ruth; Shehu, Amarda
2015-09-01
An important goal in molecular biology is to understand functional changes upon single-point mutations in proteins. Doing so through a detailed characterization of structure spaces and underlying energy landscapes is desirable but continues to challenge methods based on Molecular Dynamics. In this paper we propose a novel algorithm, SIfTER, which is based instead on stochastic optimization to circumvent the computational challenge of exploring the breadth of a protein's structure space. SIfTER is a data-driven evolutionary algorithm, leveraging experimentally-available structures of wildtype and variant sequences of a protein to define a reduced search space from where to efficiently draw samples corresponding to novel structures not directly observed in the wet laboratory. The main advantage of SIfTER is its ability to rapidly generate conformational ensembles, thus allowing mapping and juxtaposing landscapes of variant sequences and relating observed differences to functional changes. We apply SIfTER to variant sequences of the H-Ras catalytic domain, due to the prominent role of the Ras protein in signaling pathways that control cell proliferation, its well-studied conformational switching, and abundance of documented mutations in several human tumors. Many Ras mutations are oncogenic, but detailed energy landscapes have not been reported until now. Analysis of SIfTER-computed energy landscapes for the wildtype and two oncogenic variants, G12V and Q61L, suggests that these mutations cause constitutive activation through two different mechanisms. G12V directly affects binding specificity while leaving the energy landscape largely unchanged, whereas Q61L has pronounced, starker effects on the landscape. An implementation of SIfTER is made available at http://www.cs.gmu.edu/~ashehu/?q=OurTools. We believe SIfTER is useful to the community to answer the question of how sequence mutations affect the function of a protein, when there is an abundance of experimental
Clausen, Rudy; Ma, Buyong; Nussinov, Ruth; Shehu, Amarda
2015-01-01
An important goal in molecular biology is to understand functional changes upon single-point mutations in proteins. Doing so through a detailed characterization of structure spaces and underlying energy landscapes is desirable but continues to challenge methods based on Molecular Dynamics. In this paper we propose a novel algorithm, SIfTER, which is based instead on stochastic optimization to circumvent the computational challenge of exploring the breadth of a protein’s structure space. SIfTER is a data-driven evolutionary algorithm, leveraging experimentally-available structures of wildtype and variant sequences of a protein to define a reduced search space from where to efficiently draw samples corresponding to novel structures not directly observed in the wet laboratory. The main advantage of SIfTER is its ability to rapidly generate conformational ensembles, thus allowing mapping and juxtaposing landscapes of variant sequences and relating observed differences to functional changes. We apply SIfTER to variant sequences of the H-Ras catalytic domain, due to the prominent role of the Ras protein in signaling pathways that control cell proliferation, its well-studied conformational switching, and abundance of documented mutations in several human tumors. Many Ras mutations are oncogenic, but detailed energy landscapes have not been reported until now. Analysis of SIfTER-computed energy landscapes for the wildtype and two oncogenic variants, G12V and Q61L, suggests that these mutations cause constitutive activation through two different mechanisms. G12V directly affects binding specificity while leaving the energy landscape largely unchanged, whereas Q61L has pronounced, starker effects on the landscape. An implementation of SIfTER is made available at http://www.cs.gmu.edu/~ashehu/?q=OurTools. We believe SIfTER is useful to the community to answer the question of how sequence mutations affect the function of a protein, when there is an abundance of experimental
Huang, Lei; Liao, Li; Wu, Cathy H.
2016-01-01
Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273
Samei, Ehsan; Richard, Samuel
2015-01-15
Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD, Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR
Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan
2015-10-01
The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.
NASA Astrophysics Data System (ADS)
Kim, Young-Joon
2000-09-01
An algorithm is introduced to remove the directional ambiguities in ocean surface winds measured by scatterometers, which requires scatterometer data only. It is based on two versions of PBL (planetary boundary layer) models and a low-pass filter. A pressure field is first derived from the median-filtered scatterometer winds, is then noise-filtered, and is finally converted back to the winds, respectively, by an inverted PBL model, a smoothing algorithm, and a PBL model. The derived wind field is used to remove the directional ambiguities in the scatterometer data. This new algorithm is applied to Hurricane Eugene and produces results comparable to those from the current standard ambiguity removal algorithm for NASA/JPL SeaWinds project, which requires external numerical weather forecast/analyses data.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-10-01
The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI(λ)D(μ) controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications. PMID:21621208
NASA Astrophysics Data System (ADS)
Primorac, E.; Kuhlenbeck, H.; Freund, H.-J.
2016-07-01
The structure of a thin MoO3 layer on Au(111) with a c(4 × 2) superstructure was studied with LEED I/V analysis. As proposed previously (Quek et al., Surf. Sci. 577 (2005) L71), the atomic structure of the layer is similar to that of a MoO3 single layer as found in regular α-MoO3. The layer on Au(111) has a glide plane parallel to the short unit vector of the c(4 × 2) unit cell and the molybdenum atoms are bridge-bonded to two surface gold atoms with the structure of the gold surface being slightly distorted. The structural refinement of the structure was performed with the CMA-ES evolutionary strategy algorithm which could reach a Pendry R-factor of ∼ 0.044. In the second part the performance of CMA-ES is compared with that of the differential evolution method, a genetic algorithm and the Powell optimization algorithm employing I/V curves calculated with tensor LEED.
NASA Technical Reports Server (NTRS)
Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.
NASA Astrophysics Data System (ADS)
Sofiev, M.; Vira, J.; Kouznetsov, R.; Prank, M.; Soares, J.; Genikhovich, E.
2015-03-01
The paper presents dynamic cores v.4 and v.5 of the System for Integrated modeLling of Atmospheric coMposition SILAM v.5.5 based on the advection algorithm of Michael Galperin. This advection routine, so far weakly presented in international literature, is non-diffusive, positively defined, stable with regard to Courant number significantly above one, and very efficient computationally. For the first time, we present a rigorous description of its original version, along with several updates that improve its monotonicity and allow applications to long-living species in conditions of complex atmospheric flows. The other extension allows the scheme application to dynamics of aerosol spectra. The scheme is accompanied with the previously developed vertical diffusion algorithm, which encapsulates the dry deposition process as a boundary condition. Connection to chemical transformation modules is outlined, accounting for the specifics of transport scheme. Quality of the advection routine is evaluated using a large set of tests. The original approach has been previously compared with several classic algorithms widely used in operational models. The basic tests were repeated for the updated scheme, along with demanding global 2-D tests recently suggested in literature, which allowed positioning the scheme with regard to sophisticated state-of-the-art approaches. The model performance appeared close to the top of the list with very modest computational costs.
NASA Astrophysics Data System (ADS)
Shen, Xin; Zhang, Jing; Yao, Huang
2015-12-01
Remote sensing satellites play an increasingly prominent role in environmental monitoring and disaster rescue. Taking advantage of almost the same sunshine condition to same place and global coverage, most of these satellites are operated on the sun-synchronous orbit. However, it brings some problems inevitably, the most significant one is that the temporal resolution of sun-synchronous orbit satellite can't satisfy the demand of specific region monitoring mission. To overcome the disadvantages, two methods are exploited: the first one is to build satellite constellation which contains multiple sunsynchronous satellites, just like the CHARTER mechanism has done; the second is to design non-predetermined orbit based on the concrete mission demand. An effective method for remote sensing satellite orbit design based on multiobjective evolution algorithm is presented in this paper. Orbit design problem is converted into a multi-objective optimization problem, and a fast and elitist multi-objective genetic algorithm is utilized to solve this problem. Firstly, the demand of the mission is transformed into multiple objective functions, and the six orbit elements of the satellite are taken as genes in design space, then a simulate evolution process is performed. An optimal resolution can be obtained after specified generation via evolution operation (selection, crossover, and mutation). To examine validity of the proposed method, a case study is introduced: Orbit design of an optical satellite for regional disaster monitoring, the mission demand include both minimizing the average revisit time internal of two objectives. The simulation result shows that the solution for this mission obtained by our method meet the demand the users' demand. We can draw a conclusion that the method presented in this paper is efficient for remote sensing orbit design.
Long-Boyle, Janel; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J.; Dvorak, Christopher C.
2014-01-01
Background Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared to conventional dosing. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration-at-steady-state, Css) and implement a simple, model-based tool for the initial dosing of busulfan in children undergoing HCT. Patients and Methods Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone HCT with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the non-linear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly, Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Results Modeling of busulfan time-concentration data indicates busulfan CL displays non-linearity in children, decreasing up to approximately 20% between the concentrations of 250–2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan CL were actual body weight and age. The percentage of individuals achieving a therapeutic Css was significantly higher in subjects receiving initial doses based on the population PK model (81%) versus historical controls dosed on conventional guidelines (52%) (p = 0.02). Conclusion When compared to the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults. PMID:25162216
Toward a theory of evolutionary computation.
Eberbach, Eugene
2005-10-01
We outline a theory of evolutionary computation using a formal model of evolutionary computation--the Evolutionary Turing Machine--which is introduced as the extension of the Turing Machine model. Evolutionary Turing Machines provide a better and a more complete model for evolutionary computing than conventional Turing Machines, algorithms, and Markov chains. The convergence and convergence rate are defined and investigated in terms of this new model. The sufficient conditions needed for the completeness and optimality of evolutionary search are investigated. In particular, the notion of the total optimality as an instance of the multiobjective optimization of the Universal Evolutionary Turing Machine is introduced. This provides an automatic way to deal with the intractability of evolutionary search by optimizing the quality of solutions and search costs simultaneously. Based on a new model a very flexible classification of optimization problem hardness for the evolutionary techniques is proposed. The expressiveness of evolutionary computation is investigated. We show that the problem of the best evolutionary algorithm is undecidable independently of whether the fitness function is time dependent or fixed. It is demonstrated that the evolutionary computation paradigm is more expressive than Turing Machines, and thus the conventional computer science based on them. We show that an Evolutionary Turing Machine is able to solve nonalgorithmically the halting problem of the Universal Turing Machine and, asymptotically, the best evolutionary algorithm problem. In other words, the best evolutionary algorithm does not exist, but it can be potentially indefinitely approximated using evolutionary techniques.
NASA Astrophysics Data System (ADS)
Rodrigo, Deepal
2007-12-01
This dissertation introduces a novel approach for optimally operating a day-ahead electricity market not only by economically dispatching the generation resources but also by minimizing the influences of market manipulation attempts by the individual generator-owning companies while ensuring that the power system constraints are not violated. Since economic operation of the market conflicts with the individual profit maximization tactics such as market manipulation by generator-owning companies, a methodology that is capable of simultaneously optimizing these two competing objectives has to be selected. Although numerous previous studies have been undertaken on the economic operation of day-ahead markets and other independent studies have been conducted on the mitigation of market power, the operation of a day-ahead electricity market considering these two conflicting objectives simultaneously has not been undertaken previously. These facts provided the incentive and the novelty for this study. A literature survey revealed that many of the traditional solution algorithms convert multi-objective functions into either a single-objective function using weighting schemas or undertake optimization of one function at a time. Hence, these approaches do not truly optimize the multi-objectives concurrently. Due to these inherent deficiencies of the traditional algorithms, the use of alternative non-traditional solution algorithms for such problems has become popular and widely used. Of these, multi-objective evolutionary algorithms (MOEA) have received wide acceptance due to their solution quality and robustness. In the present research, three distinct algorithms were considered: a non-dominated sorting genetic algorithm II (NSGA II), a multi-objective tabu search algorithm (MOTS) and a hybrid of multi-objective tabu search and genetic algorithm (MOTS/GA). The accuracy and quality of the results from these algorithms for applications similar to the problem investigated here
Kurkcuoglu, Zeynep; Doruker, Pemra
2016-01-01
Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7-15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4-3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its "parents" up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5-2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions. PMID:27348230
Kurkcuoglu, Zeynep; Doruker, Pemra
2016-01-01
Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7–15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4–3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its “parents” up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5–2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions. PMID:27348230
Hastie, David I.; Zeller, Tanja; Liquet, Benoit; Newcombe, Paul; Yengo, Loic; Wild, Philipp S.; Schillert, Arne; Ziegler, Andreas; Nielsen, Sune F.; Butterworth, Adam S.; Ho, Weang Kee; Castagné, Raphaële; Munzel, Thomas; Tregouet, David; Falchi, Mario; Cambien, François; Nordestgaard, Børge G.; Fumeron, Fredéric; Tybjærg-Hansen, Anne; Froguel, Philippe; Danesh, John; Petretto, Enrico; Blankenberg, Stefan; Tiret, Laurence; Richardson, Sylvia
2013-01-01
Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This
NASA Astrophysics Data System (ADS)
Ayala, Helon Vicente Hultmann; Coelho, Leandro dos Santos
2016-02-01
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach.
NASA Astrophysics Data System (ADS)
Selouani, Sid-Ahmed; O'Shaughnessy, Douglas
2003-12-01
Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.
NASA Astrophysics Data System (ADS)
Moradi, M.; Delavar, M. R.; Moradi, A.
2015-12-01
Being one of the natural disasters, earthquake can seriously damage buildings, urban facilities and cause road blockage. Post-earthquake route planning is problem that has been addressed in frequent researches. The main aim of this research is to present a route planning model for after earthquake. It is assumed in this research that no damage data is available. The presented model tries to find the optimum route based on a number of contributing factors which mainly indicate the length, width and safety of the road. The safety of the road is represented by a number of criteria such as distance to faults, percentage of non-standard buildings and percentage of high buildings around the route. An integration of genetic algorithm and ordered weighted averaging operator is employed in the model. The former searches the problem space among all alternatives, while the latter aggregates the scores of road segments to compute an overall score for each alternative. Ordered weighted averaging operator enables the users of the system to evaluate the alternative routes based on their decision strategy. Based on the proposed model, an optimistic user tries to find the shortest path between the two points, whereas a pessimistic user tends to pay more attention to safety parameters even if it enforces a longer route. The results depicts that decision strategy can considerably alter the optimum route. Moreover, post-earthquake route planning is a function of not only the length of the route but also the probability of the road blockage.
Herman, Matthew R; Nejadhashemi, A Pouyan; Daneshvar, Fariborz; Abouali, Mohammad; Ross, Dennis M; Woznicki, Sean A; Zhang, Zhen
2016-10-01
The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest. PMID:27420165
Herman, Matthew R; Nejadhashemi, A Pouyan; Daneshvar, Fariborz; Abouali, Mohammad; Ross, Dennis M; Woznicki, Sean A; Zhang, Zhen
2016-10-01
The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest.
Evolutionary tree reconstruction
NASA Technical Reports Server (NTRS)
Cheeseman, Peter; Kanefsky, Bob
1990-01-01
It is described how Minimum Description Length (MDL) can be applied to the problem of DNA and protein evolutionary tree reconstruction. If there is a set of mutations that transform a common ancestor into a set of the known sequences, and this description is shorter than the information to encode the known sequences directly, then strong evidence for an evolutionary relationship has been found. A heuristic algorithm is described that searches for the simplest tree (smallest MDL) that finds close to optimal trees on the test data. Various ways of extending the MDL theory to more complex evolutionary relationships are discussed.
NASA Astrophysics Data System (ADS)
S, Kyriacou; E, Kontoleontos; S, Weissenberger; L, Mangani; E, Casartelli; I, Skouteropoulou; M, Gattringer; A, Gehrer; M, Buchmayr
2014-03-01
An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure.
Paduszyński, Kamil
2016-08-22
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many applications of ILs, particularly in separations. Three new models are proposed, each of them based on distinct machine learning algorithm: stepwise multiple linear regression (SWMLR), feed-forward artificial neural network (FFANN), and least-squares support vector machine (LSSVM). The models were established based on the most comprehensive γ(∞) data bank reported so far (>34 000 data points for 188 ILs and 128 solutes). Following the paper published previously [J. Chem. Inf. Model 2014, 54, 1311-1324], the ILs were treated in terms of group contributions, whereas the Abraham solvation parameters were used to quantify an impact of solute structure. Temperature is also included in the input data of the models so that they can be utilized to obtain temperature-dependent data and thus related thermodynamic functions. Both internal and external validation techniques were applied to assess the statistical significance and explanatory power of the final correlations. A comparative study of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches is presented in terms of root-mean-square error and average absolute relative deviation between calculated and experimental γ(∞), evaluated for different families of ILs and solutes, as well as between calculated and experimental infinite dilution selectivity for separation problems benzene from n-hexane and thiophene from n-heptane. LSSVM is shown to be a method with the lowest values of both training and generalization errors. It is finally demonstrated that the established models exhibit an improved accuracy compared to the state-of-the-art model, namely, temperature-dependent group contribution linear solvation energy relationship, published in 2011 [J. Chem
Paduszyński, Kamil
2016-08-22
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many applications of ILs, particularly in separations. Three new models are proposed, each of them based on distinct machine learning algorithm: stepwise multiple linear regression (SWMLR), feed-forward artificial neural network (FFANN), and least-squares support vector machine (LSSVM). The models were established based on the most comprehensive γ(∞) data bank reported so far (>34 000 data points for 188 ILs and 128 solutes). Following the paper published previously [J. Chem. Inf. Model 2014, 54, 1311-1324], the ILs were treated in terms of group contributions, whereas the Abraham solvation parameters were used to quantify an impact of solute structure. Temperature is also included in the input data of the models so that they can be utilized to obtain temperature-dependent data and thus related thermodynamic functions. Both internal and external validation techniques were applied to assess the statistical significance and explanatory power of the final correlations. A comparative study of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches is presented in terms of root-mean-square error and average absolute relative deviation between calculated and experimental γ(∞), evaluated for different families of ILs and solutes, as well as between calculated and experimental infinite dilution selectivity for separation problems benzene from n-hexane and thiophene from n-heptane. LSSVM is shown to be a method with the lowest values of both training and generalization errors. It is finally demonstrated that the established models exhibit an improved accuracy compared to the state-of-the-art model, namely, temperature-dependent group contribution linear solvation energy relationship, published in 2011 [J. Chem
Model-based tomographic reconstruction
Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.
2012-06-26
A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.
Bishop, Christopher M
2013-02-13
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612
Bishop, Christopher M
2013-02-13
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.
Van Uytven, Eric Van Beek, Timothy; McCowan, Peter M.; Chytyk-Praznik, Krista; Greer, Peter B.; McCurdy, Boyd M. C.
2015-12-15
Purpose: Radiation treatments are trending toward delivering higher doses per fraction under stereotactic radiosurgery and hypofractionated treatment regimens. There is a need for accurate 3D in vivo patient dose verification using electronic portal imaging device (EPID) measurements. This work presents a model-based technique to compute full three-dimensional patient dose reconstructed from on-treatment EPID portal images (i.e., transmission images). Methods: EPID dose is converted to incident fluence entering the patient using a series of steps which include converting measured EPID dose to fluence at the detector plane and then back-projecting the primary source component of the EPID fluence upstream of the patient. Incident fluence is then recombined with predicted extra-focal fluence and used to calculate 3D patient dose via a collapsed-cone convolution method. This method is implemented in an iterative manner, although in practice it provides accurate results in a single iteration. The robustness of the dose reconstruction technique is demonstrated with several simple slab phantom and nine anthropomorphic phantom cases. Prostate, head and neck, and lung treatments are all included as well as a range of delivery techniques including VMAT and dynamic intensity modulated radiation therapy (IMRT). Results: Results indicate that the patient dose reconstruction algorithm compares well with treatment planning system computed doses for controlled test situations. For simple phantom and square field tests, agreement was excellent with a 2%/2 mm 3D chi pass rate ≥98.9%. On anthropomorphic phantoms, the 2%/2 mm 3D chi pass rates ranged from 79.9% to 99.9% in the planning target volume (PTV) region and 96.5% to 100% in the low dose region (>20% of prescription, excluding PTV and skin build-up region). Conclusions: An algorithm to reconstruct delivered patient 3D doses from EPID exit dosimetry measurements was presented. The method was applied to phantom and patient
NASA Astrophysics Data System (ADS)
Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten
2016-04-01
This contribution presents a framework, which enables the use of an Evolutionary Algorithm (EA) for the calibration and regionalization of the hydrological model COSEROreg. COSEROreg uses an updated version of the HBV-type model COSERO (Kling et al. 2014) for the modelling of hydrological processes and is embedded in a parameter regionalization scheme based on Samaniego et al. (2010). The latter uses subscale-information to estimate model via a-priori chosen transfer functions (often derived from pedotransfer functions). However, the transferability of the regionalization scheme to different model-concepts and the integration of new forms of subscale information is not straightforward. (i) The usefulness of (new) single sub-scale information layers is unknown beforehand. (ii) Additionally, the establishment of functional relationships between these (possibly meaningless) sub-scale information layers and the distributed model parameters remain a central challenge in the implementation of a regionalization procedure. The proposed method theoretically provides a framework to overcome this challenge. The implementation of the EA encompasses the following procedure: First, a formal grammar is specified (Ryan et al., 1998). The construction of the grammar thereby defines the set of possible transfer functions and also allows to incorporate hydrological domain knowledge into the search itself. The EA iterates over the given space by combining parameterized basic functions (e.g. linear- or exponential functions) and sub-scale information layers into transfer functions, which are then used in COSEROreg. However, a pre-selection model is applied beforehand to sort out unfeasible proposals by the EA and to reduce the necessary model runs. A second optimization routine is used to optimize the parameters of the transfer functions proposed by the EA. This concept, namely using two nested optimization loops, is inspired by the idea of Lamarckian Evolution and Baldwin Effect
Hunt, Tam
2014-01-01
Evolution as an idea has a lengthy history, even though the idea of evolution is generally associated with Darwin today. Rebecca Stott provides an engaging and thoughtful overview of this history of evolutionary thinking in her 2013 book, Darwin's Ghosts: The Secret History of Evolution. Since Darwin, the debate over evolution—both how it takes place and, in a long war of words with religiously-oriented thinkers, whether it takes place—has been sustained and heated. A growing share of this debate is now devoted to examining how evolutionary thinking affects areas outside of biology. How do our lives change when we recognize that all is in flux? What can we learn about life more generally if we study change instead of stasis? Carter Phipps’ book, Evolutionaries: Unlocking the Spiritual and Cultural Potential of Science's Greatest Idea, delves deep into this relatively new development. Phipps generally takes as a given the validity of the Modern Synthesis of evolutionary biology. His story takes us into, as the subtitle suggests, the spiritual and cultural implications of evolutionary thinking. Can religion and evolution be reconciled? Can evolutionary thinking lead to a new type of spirituality? Is our culture already being changed in ways that we don't realize by evolutionary thinking? These are all important questions and Phipps book is a great introduction to this discussion. Phipps is an author, journalist, and contributor to the emerging “integral” or “evolutionary” cultural movement that combines the insights of Integral Philosophy, evolutionary science, developmental psychology, and the social sciences. He has served as the Executive Editor of EnlightenNext magazine (no longer published) and more recently is the co-founder of the Institute for Cultural Evolution, a public policy think tank addressing the cultural roots of America's political challenges. What follows is an email interview with Phipps. PMID:26478766
Gabora, Liane; Kauffman, Stuart
2016-04-01
Dietrich and Haider (Psychonomic Bulletin & Review, 21 (5), 897-915, 2014) justify their integrative framework for creativity founded on evolutionary theory and prediction research on the grounds that "theories and approaches guiding empirical research on creativity have not been supported by the neuroimaging evidence." Although this justification is controversial, the general direction holds promise. This commentary clarifies points of disagreement and unresolved issues, and addresses mis-applications of evolutionary theory that lead the authors to adopt a Darwinian (versus Lamarckian) approach. To say that creativity is Darwinian is not to say that it consists of variation plus selection - in the everyday sense of the term - as the authors imply; it is to say that evolution is occurring because selection is affecting the distribution of randomly generated heritable variation across generations. In creative thought the distribution of variants is not key, i.e., one is not inclined toward idea A because 60 % of one's candidate ideas are variants of A while only 40 % are variants of B; one is inclined toward whichever seems best. The authors concede that creative variation is partly directed; however, the greater the extent to which variants are generated non-randomly, the greater the extent to which the distribution of variants can reflect not selection but the initial generation bias. Since each thought in a creative process can alter the selective criteria against which the next is evaluated, there is no demarcation into generations as assumed in a Darwinian model. We address the authors' claim that reduced variability and individuality are more characteristic of Lamarckism than Darwinian evolution, and note that a Lamarckian approach to creativity has addressed the challenge of modeling the emergent features associated with insight.
Evolutionary rescue beyond the models
Gomulkiewicz, Richard; Shaw, Ruth G.
2013-01-01
Laboratory model systems and mathematical models have shed considerable light on the fundamental properties and processes of evolutionary rescue. But it remains to determine the extent to which these model-based findings can help biologists predict when evolution will fail or succeed in rescuing natural populations that are facing novel conditions that threaten their persistence. In this article, we present a prospectus for transferring our basic understanding of evolutionary rescue to wild and other non-laboratory populations. Current experimental and theoretical results emphasize how the interplay between inheritance processes and absolute fitness in changed environments drive population dynamics and determine prospects of extinction. We discuss the challenge of inferring these elements of the evolutionary rescue process in field and natural settings. Addressing this challenge will contribute to a more comprehensive understanding of population persistence that combines processes of evolutionary rescue with developmental and ecological mechanisms. PMID:23209173
Gorelik, Gregory; Shackelford, Todd K
2014-08-27
In this article, we advance the concept of "evolutionary awareness," a metacognitive framework that examines human thought and emotion from a naturalistic, evolutionary perspective. We begin by discussing the evolution and current functioning of the moral foundations on which our framework rests. Next, we discuss the possible applications of such an evolutionarily-informed ethical framework to several domains of human behavior, namely: sexual maturation, mate attraction, intrasexual competition, culture, and the separation between various academic disciplines. Finally, we discuss ways in which an evolutionary awareness can inform our cross-generational activities-which we refer to as "intergenerational extended phenotypes"-by helping us to construct a better future for ourselves, for other sentient beings, and for our environment.
Damsbo, Martin; Kinnear, Brian S.; Hartings, Matthew R.; Ruhoff, Peder T.; Jarrold, Martin F.; Ratner, Mark A.
2004-01-01
We present an evolutionary method for finding the low-energy conformations of polypeptides. The application, called foldaway,is based on a generic framework and uses several evolutionary operators as well as local optimization to navigate the complex energy landscape of polypeptides. It maintains two complementary representations of the structures and uses the charmm force field for evaluating the energies. The method is applied to unsolvated Met-enkephalin and Ac-(Ala-Gly-Gly)5-Lys+H+. Unsolvated Ac-(Ala-Gly-Gly)5-Lys+H+ has been the object of recent experimental studies using ion mobility measurements. It has a flat energy landscape where helical and globular conformations have similar energies. foldaway locates several large groups of structures not found in previous molecular dynamics simulations for this peptide, including compact globular conformations, which are probably present in the experiments. However, the relative energies of the different conformations found by foldaway do not accurately match the relative energies expected from the experimental observations. PMID:15123828
Model based manipulator control
NASA Technical Reports Server (NTRS)
Petrosky, Lyman J.; Oppenheim, Irving J.
1989-01-01
The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.
Swynghedauw, B
2004-04-01
Nothing in biology makes sense except in the light of evolution. Evolutionary, or darwinian, medicine takes the view that contemporary diseases result from incompatibility between the conditions under which the evolutionary pressure had modified our genetic endowment and the lifestyle and dietary habits in which we are currently living, including the enhanced lifespan, the changes in dietary habits and the lack of physical activity. An evolutionary trait express a genetic polymorphism which finally improve fitness, it needs million years to become functional. A limited genetic diversity is a necessary prerequisite for evolutionary medicine. Nevertheless, search for a genetic endowment would become nearly impossible if the human races were genetically different. From a genetic point of view, homo sapiens, is homogeneous, and the so-called human races have only a socio-economic definition. Historically, Heart Failure, HF, had an infectious origin and resulted from mechanical overload which triggered mechanoconversion by using phylogenically ancient pleiotropic pathways. Adaptation was mainly caused by negative inotropism. Recently, HF was caused by a complex remodelling caused by the trophic effects of mechanics, ischemia, senescence, diabetes and, neurohormones. The generally admitted hypothesis is that cancers were largely caused by a combination of modern reproductive and dietary lifestyles mismatched with genotypic traits, plus the longer time available for a confrontation. Such a concept is illustrated for skin and breast cancers, and also for the link between cancer risk and dietary habits.
Model-Based Fault Tolerant Control
NASA Technical Reports Server (NTRS)
Kumar, Aditya; Viassolo, Daniel
2008-01-01
The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.
Model-based vision using geometric hashing
NASA Astrophysics Data System (ADS)
Akerman, Alexander, III; Patton, Ronald
1991-04-01
The Geometric Hashing technique developed by the NYU Courant Institute has been applied to various automatic target recognition applications. In particular, I-MATH has extended the hashing algorithm to perform automatic target recognition ofsynthetic aperture radar (SAR) imagery. For this application, the hashing is performed upon the geometric locations of dominant scatterers. In addition to being a robust model-based matching algorithm -- invariant under translation, scale, and 3D rotations of the target -- hashing is of particular utility because it can still perform effective matching when the target is partially obscured. Moreover, hashing is very amenable to a SIMD parallel processing architecture, and thus potentially realtime implementable.
Evolutionary software for autonomous path planning
Couture, S; Hage, M
1999-02-10
This research project demonstrated the effectiveness of using evolutionary software techniques in the development of path-planning algorithms and control programs for mobile vehicles in radioactive environments. The goal was to take maximum advantage of the programmer's intelligence by tasking the programmer with encoding the measures of success for a path-planning algorithm, rather than developing the path-planning algorithms themselves. Evolutionary software development techniques could then be used to develop algorithms most suitable to the particular environments of interest. The measures of path-planning success were encoded in the form of a fitness function for an evolutionary software development engine. The task for the evolutionary software development engine was to evaluate the performance of individual algorithms, select the best performers for the population based on the fitness function, and breed them to evolve the next generation of algorithms. The process continued for a set number of generations or until the algorithm converged to an optimal solution. The task environment was the navigation of a rover from an initial location to a goal, then to a processing point, in an environment containing physical and radioactive obstacles. Genetic algorithms were developed for a variety of environmental configurations. Algorithms were simple and non-robust strings of behaviors, but they could be evolved to be nearly optimal for a given environment. In addition, a genetic program was evolved in the form of a control algorithm that operates at every motion of the robot. Programs were more complex than algorithms and less optimal in a given environment. However, after training in a variety of different environments, they were more robust and could perform acceptably in environments they were not trained in. This paper describes the evolutionary software development engine and the performance of algorithms and programs evolved by it for the chosen task.
NASA Technical Reports Server (NTRS)
Frisch, Harold P.
2007-01-01
Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.
Evolutionary Dynamics of Biological Games
NASA Astrophysics Data System (ADS)
Nowak, Martin A.; Sigmund, Karl
2004-02-01
Darwinian dynamics based on mutation and selection form the core of mathematical models for adaptation and coevolution of biological populations. The evolutionary outcome is often not a fitness-maximizing equilibrium but can include oscillations and chaos. For studying frequency-dependent selection, game-theoretic arguments are more appropriate than optimization algorithms. Replicator and adaptive dynamics describe short- and long-term evolution in phenotype space and have found applications ranging from animal behavior and ecology to speciation, macroevolution, and human language. Evolutionary game theory is an essential component of a mathematical and computational approach to biology.
An Evolutionary Game Theoretic Approach for Conjunctive Surface and Ground Water Allocation
NASA Astrophysics Data System (ADS)
Parsapour Moghaddam, P.; Abed Elmdoust, A.; Kerachian, R.
2011-12-01
In this paper, a non-cooperative game theoretic methodology is developed for determining evolutionary stable policies for surface and groundwater allocation to stakeholders with conflicting objectives. In the proposed methodology, the information of water balance, hydrogeologic characteristics of the aquifer and some other crucial data are used for modeling groundwater flow using the MODFLOW and MT3D groundwater quantity and quality simulation models. An optimization model based on genetic algorithm is also developed that yields the evolutionary stable water allocation strategies for the stakeholders considering different non-cooperative common pool resources (CPR) management institutions within short and long term planning horizons. In the methodology, some basic characteristics of beneficiaries are also considered to determine how different planning variables are affected by the different rationales and exploitation strategies of stakeholders. To illustrate the practical utility of the proposed methodology, it is applied to the Rafsanjan Basin in Iran.
Evolutionary Design in Biology
NASA Astrophysics Data System (ADS)
Wiese, Kay C.
Much progress has been achieved in recent years in molecular biology and genetics. The sheer volume of data in the form of biological sequences has been enormous and efficient methods for dealing with these huge amounts of data are needed. In addition, the data alone does not provide information on the workings of biological systems; hence much research effort has focused on designing mathematical and computational models to address problems from molecular biology. Often, the terms bioinformatics and computational biology are used to refer to the research fields concerning themselves with designing solutions to molecular problems in biology. However, there is a slight distinction between bioinformatics and computational biology: the former is concerned with managing the enormous amounts of biological data and extracting information from it, while the latter is more concerned with the design and development of new algorithms to address problems such as protein or RNA folding. However, the boundary is blurry, and there is no consistent usage of the terms. We will use the term bioinformatics to encompass both fields. To cover all areas of research in bioinformatics is beyond the scope of this section and we refer the interested reader to [2] for a general introduction. A large part of what bioinformatics is concerned about is evolution and function of biological systems on a molecular level. Evolutionary computation and evolutionary design are concerned with developing computational systems that "mimic" certain aspects of natural evolution (mutation, crossover, selection, fitness). Much of the inner workings of natural evolutionary systems have been copied, sometimes in modified format into evolutionary computation systems. Artificial neural networks mimic the functioning of simple brain cell clusters. Fuzzy systems are concerned with the "fuzzyness" in decision making, similar to a human expert. These three computational paradigms fall into the category of
Neurocontroller analysis via evolutionary network minimization.
Ganon, Zohar; Keinan, Alon; Ruppin, Eytan
2006-01-01
This study presents a new evolutionary network minimization (ENM) algorithm. Neurocontroller minimization is beneficial for finding small parsimonious networks that permit a better understanding of their workings. The ENM algorithm is specifically geared to an evolutionary agents setup, as it does not require any explicit supervised training error, and is very easily incorporated in current evolutionary algorithms. ENM is based on a standard genetic algorithm with an additional step during reproduction in which synaptic connections are irreversibly eliminated. It receives as input a successfully evolved neurocontroller and aims to output a pruned neurocontroller, while maintaining the original fitness level. The small neurocontrollers produced by ENM provide upper bounds on the neurocontroller size needed to perform a given task successfully, and can provide for more effcient hardware implementations. PMID:16859448
Evolutionary engineering for industrial microbiology.
Vanee, Niti; Fisher, Adam B; Fong, Stephen S
2012-01-01
Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.
Theoretical developments in evolutionary computation
NASA Astrophysics Data System (ADS)
Fogel, David B.
1999-11-01
Recent developments in the theory of evolutionary computation offer evidence and proof that overturns several conventionally held beliefs. In particular, the no free lunch theorem and other related theorems show that there can be no best evolutionary algorithm, and that no particular variation operator or selection mechanism provides a general advantage over another choice. Furthermore, the fundamental nature of the notion of schema processing is called into question by recent theory that shows that the schema theorem does not hold when schema fitness is stochastic. Moreover, the analysis that underlies schema theory, namely the k- armed bandit analysis, does not generate a sampling plan that yields an optimal allocation of trials, as has been suggested in the literature for almost 25 years. The importance of these new findings is discussed in the context of future progress in the field of evolutionary computation.
Model-based reconfiguration: Diagnosis and recovery
NASA Technical Reports Server (NTRS)
Crow, Judy; Rushby, John
1994-01-01
We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.
Evolutionary Multiobjective Design Targeting a Field Programmable Transistor Array
NASA Technical Reports Server (NTRS)
Aguirre, Arturo Hernandez; Zebulum, Ricardo S.; Coello, Carlos Coello
2004-01-01
This paper introduces the ISPAES algorithm for circuit design targeting a Field Programmable Transistor Array (FPTA). The use of evolutionary algorithms is common in circuit design problems, where a single fitness function drives the evolution process. Frequently, the design problem is subject to several goals or operating constraints, thus, designing a suitable fitness function catching all requirements becomes an issue. Such a problem is amenable for multi-objective optimization, however, evolutionary algorithms lack an inherent mechanism for constraint handling. This paper introduces ISPAES, an evolutionary optimization algorithm enhanced with a constraint handling technique. Several design problems targeting a FPTA show the potential of our approach.
Toward a unifying framework for evolutionary processes
Paixão, Tiago; Badkobeh, Golnaz; Barton, Nick; Çörüş, Doğan; Dang, Duc-Cuong; Friedrich, Tobias; Lehre, Per Kristian; Sudholt, Dirk; Sutton, Andrew M.; Trubenová, Barbora
2015-01-01
The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. PMID:26215686
NASA Technical Reports Server (NTRS)
Rowe, Sidney E.
2010-01-01
In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.
An inquiry into evolutionary inquiry
NASA Astrophysics Data System (ADS)
Donovan, Samuel S.
2005-11-01
While evolution education has received a great deal of attention within the science education research community it still poses difficult teaching and learning challenges. Understanding evolutionary biology has been given high priority in national science education policy because of its role in coordinating our understanding of the life sciences, its importance in our intellectual history, its role in the perception of humans' position in nature, and its impact on our current medical, agricultural, and conservation practices. The rhetoric used in evolution education policy statements emphasizes familiarity with the nature of scientific inquiry as an important learning outcome associated with understanding evolution but provide little guidance with respect to how one might achieve this goal. This dissertation project explores the nature of evolutionary inquiry and how understanding the details of disciplinary reasoning can inform evolution education. The first analysis involves recasting the existing evolution education research literature to assess educational outcomes related to students ability to reason about data using evolutionary biology methods and models. This is followed in the next chapter by a detailed historical and philosophical characterization of evolutionary biology with the goal of providing a richer context for considering what exactly it is we want students to know about evolution as a discipline. Chapter 4 describes the development and implementation of a high school evolution curriculum that engages students with many aspects of model based reasoning. The final component of this reframing of evolution education involves an empirical study characterizing students' understanding of evolutionary biology as a modeling enterprise. Each chapter addresses a different aspect of evolution education and explores the implications of foregrounding disciplinary reasoning as an educational outcome. The analyses are coordinated with one another in the sense
Model-based clustered-dot screening
NASA Astrophysics Data System (ADS)
Kim, Sang Ho
2006-01-01
I propose a halftone screen design method based on a human visual system model and the characteristics of the electro-photographic (EP) printer engine. Generally, screen design methods based on human visual models produce dispersed-dot type screens while design methods considering EP printer characteristics generate clustered-dot type screens. In this paper, I propose a cost function balancing the conflicting characteristics of the human visual system and the printer. By minimizing the obtained cost function, I design a model-based clustered-dot screen using a modified direct binary search algorithm. Experimental results demonstrate the superior quality of the model-based clustered-dot screen compared to a conventional clustered-dot screen.
Principles of models based engineering
Dolin, R.M.; Hefele, J.
1996-11-01
This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.
Efficient Model-Based Diagnosis Engine
NASA Technical Reports Server (NTRS)
Fijany, Amir; Vatan, Farrokh; Barrett, Anthony; James, Mark; Mackey, Ryan; Williams, Colin
2009-01-01
An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.
Evolutionary approach to image reconstruction from projections
NASA Astrophysics Data System (ADS)
Nakao, Zensho; Ali, Fathelalem F.; Takashibu, Midori; Chen, Yen-Wei
1997-10-01
We present an evolutionary approach for reconstructing CT images; the algorithm reconstructs two-dimensional unknown images from four one-dimensional projections. A genetic algorithm works on a randomly generated population of strings each of which contains encodings of an image. The traditional, as well as new, genetic operators are applied on each generation. The mean square error between the projection data of the image encoded into a string and original projection data is used to estimate the string fitness. A Laplacian constraint term is included in the fitness function of the genetic algorithm for handling smooth images. Two new modified versions of the original genetic algorithm are presented. Results obtained by the original algorithm and the modified versions are compared to those obtained by the well-known algebraic reconstruction technique (ART), and it was found that the evolutionary method is more effective than ART in the particular case of limiting projection directions to four.
Model-based vision for space applications
NASA Technical Reports Server (NTRS)
Chaconas, Karen; Nashman, Marilyn; Lumia, Ronald
1992-01-01
This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks.
Chira, Camelia; Horvath, Dragos; Dumitrescu, D
2011-01-01
Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.
NASA Astrophysics Data System (ADS)
Huang, W. C.; Lai, C. M.; Luo, B.; Tsai, C. K.; Chih, M. H.; Lai, C. W.; Kuo, C. C.; Liu, R. G.; Lin, H. T.
2006-03-01
Optical proximity correction is the technique of pre-distorting mask layouts so that the printed patterns are as close to the desired shapes as possible. For model-based optical proximity correction, a lithographic model to predict the edge position (contour) of patterns on the wafer after lithographic processing is needed. Generally, segmentation of edges is performed prior to the correction. Pattern edges are dissected into several small segments with corresponding target points. During the correction, the edges are moved back and forth from the initial drawn position, assisted by the lithographic model, to finally settle on the proper positions. When the correction converges, the intensity predicted by the model in every target points hits the model-specific threshold value. Several iterations are required to achieve the convergence and the computation time increases with the increase of the required iterations. An artificial neural network is an information-processing paradigm inspired by biological nervous systems, such as how the brain processes information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. A neural network can be a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The network can accurately predict the behavior of a system via the learning procedure. A radial basis function network, a variant of artificial neural network, is an efficient function approximator. In this paper, a radial basis function network was used to build a mapping from the segment characteristics to the edge shift from the drawn position. This network can provide a good initial guess for each segment that OPC has carried out. The good initial guess reduces the required iterations. Consequently, cycle time can be shortened effectively. The optimization of the radial basis function network for this system was practiced by genetic algorithm
New Improved Fractional Order Differentiator Models Based on Optimized Digital Differentiators
Gupta, Maneesha
2014-01-01
Different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA), and PSO-GA hybrid optimization, have been used to optimize digital differential operators so that these can be better fitted to exemplify their new improved fractional order differentiator counterparts. First, the paper aims to provide efficient 2nd and 3rd order operators in connection with process of minimization of error fitness function by registering mean, median, and standard deviation values in different random iterations to ascertain the best results among them, using all the abovementioned EAs. Later, these optimized operators are discretized for half differentiator models for utilizing their restored qualities inhibited from their optimization. Simulation results present the comparisons of the proposed half differentiators with the existing and amongst different models based on 2nd and 3rd order optimized operators. Proposed half differentiators have been observed to approximate the ideal half differentiator and also outperform the existing ones reasonably well in complete range of Nyquist frequency. PMID:24688426
Evolutionary design of corrugated horn antennas
NASA Technical Reports Server (NTRS)
Hoorfar, F.; Manshadi, V.; Jamnejad, A.
2002-01-01
An evolutionary progranirnitzg (EP) algorithm is used to optimize pattern of a corrugated circularhorn subject to various constraints on return loss and antenna beamwidth and pattern circularity and low crosspolarization. The EP algorithm uses a Gaussian mutation operator. Examples on design synthesis of a 45 section corrugated horn, with a total of 90 optimization parameters, are presented. The results show excellent and efficient optimization of the desired horn parameters.
A comparative study of corrugated horn design by evolutionary techniques
NASA Technical Reports Server (NTRS)
Hoorfar, A.
2003-01-01
Here an evolutionary programming algorithm is used to optimize the pattern of a corrugated circular horn subject to various constraints on return loss, antenna beamwidth, pattern circularity, and low cross polarization.
Algorithms and Algorithmic Languages.
ERIC Educational Resources Information Center
Veselov, V. M.; Koprov, V. M.
This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…
Kinetic modeling based probabilistic segmentation for molecular images.
Saad, Ahmed; Hamarneh, Ghassan; Möller, Torsten; Smith, Ben
2008-01-01
We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (SNR) and partial volume effect (PVE) problems. Synthetic fluorodeoxyglucose (FDG) and simulated Raclopride dynamic positron emission tomography (dPET) brain images with excessive noise levels are used to validate our algorithm. We show, qualitatively and quantitatively, that our algorithm outperforms state-of-the-art techniques in identifying different functional regions and recovering the kinetic parameters.
Model-Based Reasoning in Humans Becomes Automatic with Training.
Economides, Marcos; Kurth-Nelson, Zeb; Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J
2015-09-01
Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders. PMID:26379239
Model-Based Reasoning in Humans Becomes Automatic with Training
Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J.
2015-01-01
Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load—a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders. PMID:26379239
From evolutionary computation to the evolution of things.
Eiben, Agoston E; Smith, Jim
2015-05-28
Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.
[Evolution of evolutionary physiology].
Natochin, Iu V
2008-09-01
In 19th century and at the beginning 20th century, reports appeared in the field of comparative and ontogenetic physiology and the value of these methods for understanding of evolution of functions. The term "evolutionary physiology" was suggested by A. N. Severtsov in 1914. In the beginning of 30s, in the USSR, laboratories for researches in problems of evolutionary physiology were created, the results of these researches having been published. In 1956 in Leningrad, the Institute of Evolutionary Physiology was founded by L. A. Orbeli. He formulates the goals and methods of evolutionary physiology. In the following half a century, the evolutionary physiology was actively developed. The evolutionary physiology solves problems of evolution of function of functions evolution, often involving methods of adjacent sciences, including biochemistry, morphology, molecular biology.
On evolutionary causes and evolutionary processes.
Laland, Kevin N
2015-08-01
In this essay I consider how biologists understand 'causation' and 'evolutionary process', drawing attention to some idiosyncrasies in the use of these terms. I suggest that research within the evolutionary sciences has been channeled in certain directions and not others by scientific conventions, many of which have now become counterproductive. These include the views (i) that evolutionary processes are restricted to those phenomena that directly change gene frequencies, (ii) that understanding the causes of both ecological change and ontogeny is beyond the remit of evolutionary biology, and (iii) that biological causation can be understood by a dichotomous proximate-ultimate distinction, with developmental processes perceived as solely relevant to proximate causation. I argue that the notion of evolutionary process needs to be broadened to accommodate phenomena such as developmental bias and niche construction that bias the course of evolution, but do not directly change gene frequencies, and that causation in biological systems is fundamentally reciprocal in nature. This article is part of a Special Issue entitled: In Honor of Jerry Hogan.
Multilogistic regression by means of evolutionary product-unit neural networks.
Hervás-Martínez, C; Martínez-Estudillo, F J; Carbonero-Ruz, M
2008-09-01
We propose a multilogistic regression model based on the combination of linear and product-unit models, where the product-unit nonlinear functions are constructed with the product of the inputs raised to arbitrary powers. The estimation of the coefficients of the model is carried out in two phases. First, the number of product-unit basis functions and the exponents' vector are determined by means of an evolutionary neural network algorithm. Afterwards, a standard maximum likelihood optimization method determines the rest of the coefficients in the new space given by the initial variables and the product-unit basis functions previously estimated. We compare the performance of our approach with the logistic regression built on the initial variables and several learning classification techniques. The statistical test carried out on twelve benchmark datasets shows that the proposed model is competitive in terms of the accuracy of the classifier.
Remembering the evolutionary Freud.
Young, Allan
2006-03-01
Throughout his career as a writer, Sigmund Freud maintained an interest in the evolutionary origins of the human mind and its neurotic and psychotic disorders. In common with many writers then and now, he believed that the evolutionary past is conserved in the mind and the brain. Today the "evolutionary Freud" is nearly forgotten. Even among Freudians, he is regarded to be a red herring, relevant only to the extent that he diverts attention from the enduring achievements of the authentic Freud. There are three ways to explain these attitudes. First, the evolutionary Freud's key work is the "Overview of the Transference Neurosis" (1915). But it was published at an inopportune moment, forty years after the author's death, during the so-called "Freud wars." Second, Freud eventually lost interest in the "Overview" and the prospect of a comprehensive evolutionary theory of psychopathology. The publication of The Ego and the Id (1923), introducing Freud's structural theory of the psyche, marked the point of no return. Finally, Freud's evolutionary theory is simply not credible. It is based on just-so stories and a thoroughly discredited evolutionary mechanism, Lamarckian use-inheritance. Explanations one and two are probably correct but also uninteresting. Explanation number three assumes that there is a fundamental difference between Freud's evolutionary narratives (not credible) and the evolutionary accounts of psychopathology that currently circulate in psychiatry and mainstream journals (credible). The assumption is mistaken but worth investigating.
Expansion of biological pathways based on evolutionary inference
Li, Yang; Calvo, Sarah E.; Gutman, Roee
2014-01-01
Summary Availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history, but rather, consist of distinct “evolutionary modules.” We introduce a computational algorithm, CLIME (clustering by inferred models of evolution), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1000 annotated human pathways, organelles and proteomes of yeast, red algae, and malaria, reveals unanticipated evolutionary modularity and novel, co-evolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes. PMID:24995987
NASA Technical Reports Server (NTRS)
Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.
2006-01-01
System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.
Polymorphic Evolutionary Games.
Fishman, Michael A
2016-06-01
In this paper, I present an analytical framework for polymorphic evolutionary games suitable for explicitly modeling evolutionary processes in diploid populations with sexual reproduction. The principal aspect of the proposed approach is adding diploid genetics cum sexual recombination to a traditional evolutionary game, and switching from phenotypes to haplotypes as the new game׳s pure strategies. Here, the relevant pure strategy׳s payoffs derived by summing the payoffs of all the phenotypes capable of producing gametes containing that particular haplotype weighted by the pertinent probabilities. The resulting game is structurally identical to the familiar Evolutionary Games with non-linear pure strategy payoffs (Hofbauer and Sigmund, 1998. Cambridge University Press), and can be analyzed in terms of an established analytical framework for such games. And these results can be translated into the terms of genotypic, and whence, phenotypic evolutionary stability pertinent to the original game.
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
ERIC Educational Resources Information Center
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
NASA Astrophysics Data System (ADS)
Senkerik, Roman; Zelinka, Ivan; Davendra, Donald; Oplatkova, Zuzana
2010-06-01
This research deals with the optimization of the control of chaos by means of evolutionary algorithms. This work is aimed on an explanation of how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing very fast and precise stabilization of desired state for any initial conditions. As a model of deterministic chaotic system, the one dimensional Logistic equation was used. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF.
Evolutionary biology of language.
Nowak, M A
2000-01-01
Language is the most important evolutionary invention of the last few million years. It was an adaptation that helped our species to exchange information, make plans, express new ideas and totally change the appearance of the planet. How human language evolved from animal communication is one of the most challenging questions for evolutionary biology The aim of this paper is to outline the major principles that guided language evolution in terms of mathematical models of evolutionary dynamics and game theory. I will discuss how natural selection can lead to the emergence of arbitrary signs, the formation of words and syntactic communication. PMID:11127907
Evolutionary Mechanisms for Loneliness
Cacioppo, John T.; Cacioppo, Stephanie; Boomsma, Dorret I.
2013-01-01
Robert Weiss (1973) conceptualized loneliness as perceived social isolation, which he described as a gnawing, chronic disease without redeeming features. On the scale of everyday life, it is understandable how something as personally aversive as loneliness could be regarded as a blight on human existence. However, evolutionary time and evolutionary forces operate at such a different scale of organization than we experience in everyday life that personal experience is not sufficient to understand the role of loneliness in human existence. Research over the past decade suggests a very different view of loneliness than suggested by personal experience, one in which loneliness serves a variety of adaptive functions in specific habitats. We review evidence on the heritability of loneliness and outline an evolutionary theory of loneliness, with an emphasis on its potential adaptive value in an evolutionary timescale. PMID:24067110
Evolutionary behavioral genetics
Zietsch, Brendan P.; de Candia, Teresa R; Keller, Matthew C.
2014-01-01
We describe the scientific enterprise at the intersection of evolutionary psychology and behavioral genetics—a field that could be termed Evolutionary Behavioral Genetics—and how modern genetic data is revolutionizing our ability to test questions in this field. We first explain how genetically informative data and designs can be used to investigate questions about the evolution of human behavior, and describe some of the findings arising from these approaches. Second, we explain how evolutionary theory can be applied to the investigation of behavioral genetic variation. We give examples of how new data and methods provide insight into the genetic architecture of behavioral variation and what this tells us about the evolutionary processes that acted on the underlying causal genetic variants. PMID:25587556
Rethinking evolutionary individuality
Ereshefsky, Marc; Pedroso, Makmiller
2015-01-01
This paper considers whether multispecies biofilms are evolutionary individuals. Numerous multispecies biofilms have characteristics associated with individuality, such as internal integrity, division of labor, coordination among parts, and heritable adaptive traits. However, such multispecies biofilms often fail standard reproductive criteria for individuality: they lack reproductive bottlenecks, are comprised of multiple species, do not form unified reproductive lineages, and fail to have a significant division of reproductive labor among their parts. If such biofilms are good candidates for evolutionary individuals, then evolutionary individuality is achieved through other means than frequently cited reproductive processes. The case of multispecies biofilms suggests that standard reproductive requirements placed on individuality should be reconsidered. More generally, the case of multispecies biofilms indicates that accounts of individuality that focus on single-species eukaryotes are too restrictive and that a pluralistic and open-ended account of evolutionary individuality is needed. PMID:26039982
Automated Hardware Design via Evolutionary Search
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Colombano, Silvano P.
2000-01-01
The goal of this research is to investigate the application of evolutionary search to the process of automated engineering design. Evolutionary search techniques involve the simulation of Darwinian mechanisms by computer algorithms. In recent years, such techniques have attracted much attention because they are able to tackle a wide variety of difficult problems and frequently produce acceptable solutions. The results obtained are usually functional, often surprising, and typically "messy" because the algorithms are told to concentrate on the overriding objective and not elegance or simplicity. advantages. First, faster design cycles translate into time and, hence, cost savings. Second, automated design techniques can be made to scale well and hence better deal with increasing amounts of design complexity. Third, design quality can increase because design properties can be specified a priori. For example, size and weight specifications of a device, smaller and lighter than the best known design, might be optimized by the automated design technique. The domain of electronic circuit design is an advantageous platform in which to study automated design techniques because it is a rich design space that is well understood, permitting human-created designs to be compared to machine- generated designs. developed for circuit design was to automatically produce high-level integrated electronic circuit designs whose properties permit physical implementation in silicon. This process entailed designing an effective evolutionary algorithm and solving a difficult multiobjective optimization problem. FY 99 saw many accomplishments in this effort.
The major evolutionary transitions.
Szathmáry, E; Smith, J M
1995-03-16
There is no theoretical reason to expect evolutionary lineages to increase in complexity with time, and no empirical evidence that they do so. Nevertheless, eukaryotic cells are more complex than prokaryotic ones, animals and plants are more complex than protists, and so on. This increase in complexity may have been achieved as a result of a series of major evolutionary transitions. These involved changes in the way information is stored and transmitted.
Model Based Autonomy for Robust Mars Operations
NASA Technical Reports Server (NTRS)
Kurien, James A.; Nayak, P. Pandurang; Williams, Brian C.; Lau, Sonie (Technical Monitor)
1998-01-01
Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.
Model-based sensor-less wavefront aberration correction in optical coherence tomography.
Verstraete, Hans R G W; Wahls, Sander; Kalkman, Jeroen; Verhaegen, Michel
2015-12-15
Several sensor-less wavefront aberration correction methods that correct nonlinear wavefront aberrations by maximizing the optical coherence tomography (OCT) signal are tested on an OCT setup. A conventional coordinate search method is compared to two model-based optimization methods. The first model-based method takes advantage of the well-known optimization algorithm (NEWUOA) and utilizes a quadratic model. The second model-based method (DONE) is new and utilizes a random multidimensional Fourier-basis expansion. The model-based algorithms achieve lower wavefront errors with up to ten times fewer measurements. Furthermore, the newly proposed DONE method outperforms the NEWUOA method significantly. The DONE algorithm is tested on OCT images and shows a significantly improved image quality. PMID:26670496
Skeletal maturity determination from hand radiograph by model-based analysis
NASA Astrophysics Data System (ADS)
Vogelsang, Frank; Kohnen, Michael; Schneider, Hansgerd; Weiler, Frank; Kilbinger, Markus W.; Wein, Berthold B.; Guenther, Rolf W.
2000-06-01
Derived from a model based segmentation algorithm for hand radiographs proposed in our former work we now present a method to determine skeletal maturity by an automated analysis of regions of interest (ROI). These ROIs including the epiphyseal and carpal bones, which are most important for skeletal maturity determination, can be extracted out of the radiograph by knowledge based algorithms.
Evolutionary processes and mother-child attachment in intentional change.
Ho, S Shaun; Torres-Garcia, Adrianna; Swain, James E
2014-08-01
Behavioral change may occur through evolutionary processes such as running stochastic evolutionary algorithms, with a fitness function to determine a winning solution from many. A science of intentional change will therefore require identification of fitness functions - causal mechanisms of adaptation - that can be acquired only with analytical approaches. Fitness functions may be subject to early-life experiences with parents, which influence some of the very same brain circuits that may mediate behavioral change through interventions. PMID:25162871
3-D model-based vehicle tracking.
Lou, Jianguang; Tan, Tieniu; Hu, Weiming; Yang, Hao; Maybank, Steven J
2005-10-01
This paper aims at tracking vehicles from monocular intensity image sequences and presents an efficient and robust approach to three-dimensional (3-D) model-based vehicle tracking. Under the weak perspective assumption and the ground-plane constraint, the movements of model projection in the two-dimensional image plane can be decomposed into two motions: translation and rotation. They are the results of the corresponding movements of 3-D translation on the ground plane (GP) and rotation around the normal of the GP, which can be determined separately. A new metric based on point-to-line segment distance is proposed to evaluate the similarity between an image region and an instantiation of a 3-D vehicle model under a given pose. Based on this, we provide an efficient pose refinement method to refine the vehicle's pose parameters. An improved EKF is also proposed to track and to predict vehicle motion with a precise kinematics model. Experimental results with both indoor and outdoor data show that the algorithm obtains desirable performance even under severe occlusion and clutter.
Model-Based Method for Sensor Validation
NASA Technical Reports Server (NTRS)
Vatan, Farrokh
2012-01-01
Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).
3-D model-based vehicle tracking.
Lou, Jianguang; Tan, Tieniu; Hu, Weiming; Yang, Hao; Maybank, Steven J
2005-10-01
This paper aims at tracking vehicles from monocular intensity image sequences and presents an efficient and robust approach to three-dimensional (3-D) model-based vehicle tracking. Under the weak perspective assumption and the ground-plane constraint, the movements of model projection in the two-dimensional image plane can be decomposed into two motions: translation and rotation. They are the results of the corresponding movements of 3-D translation on the ground plane (GP) and rotation around the normal of the GP, which can be determined separately. A new metric based on point-to-line segment distance is proposed to evaluate the similarity between an image region and an instantiation of a 3-D vehicle model under a given pose. Based on this, we provide an efficient pose refinement method to refine the vehicle's pose parameters. An improved EKF is also proposed to track and to predict vehicle motion with a precise kinematics model. Experimental results with both indoor and outdoor data show that the algorithm obtains desirable performance even under severe occlusion and clutter. PMID:16238061
MODEL-BASED CLUSTERING OF LARGE NETWORKS1
Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael
2015-01-01
We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables. PMID:26605002
Fast model-based estimation of ancestry in unrelated individuals.
Alexander, David H; Novembre, John; Lange, Kenneth
2009-09-01
Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
Proteomics in evolutionary ecology.
Baer, B; Millar, A H
2016-03-01
Evolutionary ecologists are traditionally gene-focused, as genes propagate phenotypic traits across generations and mutations and recombination in the DNA generate genetic diversity required for evolutionary processes. As a consequence, the inheritance of changed DNA provides a molecular explanation for the functional changes associated with natural selection. A direct focus on proteins on the other hand, the actual molecular agents responsible for the expression of a phenotypic trait, receives far less interest from ecologists and evolutionary biologists. This is partially due to the central dogma of molecular biology that appears to define proteins as the 'dead-end of molecular information flow' as well as technical limitations in identifying and studying proteins and their diversity in the field and in many of the more exotic genera often favored in ecological studies. Here we provide an overview of a newly forming field of research that we refer to as 'Evolutionary Proteomics'. We point out that the origins of cellular function are related to the properties of polypeptide and RNA and their interactions with the environment, rather than DNA descent, and that the critical role of horizontal gene transfer in evolution is more about coopting new proteins to impact cellular processes than it is about modifying gene function. Furthermore, post-transcriptional and post-translational processes generate a remarkable diversity of mature proteins from a single gene, and the properties of these mature proteins can also influence inheritance through genetic and perhaps epigenetic mechanisms. The influence of post-transcriptional diversification on evolutionary processes could provide a novel mechanistic underpinning for elements of rapid, directed evolutionary changes and adaptations as observed for a variety of evolutionary processes. Modern state-of the art technologies based on mass spectrometry are now available to identify and quantify peptides, proteins, protein
Applying evolutionary anthropology.
Gibson, Mhairi A; Lawson, David W
2015-01-01
Evolutionary anthropology provides a powerful theoretical framework for understanding how both current environments and legacies of past selection shape human behavioral diversity. This integrative and pluralistic field, combining ethnographic, demographic, and sociological methods, has provided new insights into the ultimate forces and proximate pathways that guide human adaptation and variation. Here, we present the argument that evolutionary anthropological studies of human behavior also hold great, largely untapped, potential to guide the design, implementation, and evaluation of social and public health policy. Focusing on the key anthropological themes of reproduction, production, and distribution we highlight classic and recent research demonstrating the value of an evolutionary perspective to improving human well-being. The challenge now comes in transforming relevance into action and, for that, evolutionary behavioral anthropologists will need to forge deeper connections with other applied social scientists and policy-makers. We are hopeful that these developments are underway and that, with the current tide of enthusiasm for evidence-based approaches to policy, evolutionary anthropology is well positioned to make a strong contribution.
Paleoanthropology and evolutionary theory.
Tattersall, Ian
2012-01-01
Paleoanthropologists of the first half of the twentieth century were little concerned either with evolutionary theory or with the technicalities and broader implications of zoological nomenclature. In consequence, the paleoanthropological literature of the period consisted largely of a series of descriptions accompanied by authoritative pronouncements, together with a huge excess of hominid genera and species. Given the intellectual flimsiness of the resulting paleoanthropological framework, it is hardly surprising that in 1950 the ornithologist Ernst Mayr met little resistance when he urged the new postwar generation of paleoanthropologists to accept not only the elegant reductionism of the Evolutionary Synthesis but a vast oversimplification of hominid phylogenetic history and nomenclature. Indeed, the impact of Mayr's onslaught was so great that even when developments in evolutionary biology during the last quarter of the century brought other paleontologists to the realization that much more has been involved in evolutionary histories than the simple action of natural selection within gradually transforming lineages, paleoanthropologists proved highly reluctant to follow. Even today, paleoanthropologists are struggling to reconcile an intuitive realization that the burgeoning hominid fossil record harbors a substantial diversity of species (bringing hominid evolutionary patterns into line with that of other successful mammalian families), with the desire to cram a huge variety of morphologies into an unrealistically minimalist systematic framework. As long as this theoretical ambivalence persists, our perception of events in hominid phylogeny will continue to be distorted.
Applying Evolutionary Anthropology
Gibson, Mhairi A; Lawson, David W
2015-01-01
Evolutionary anthropology provides a powerful theoretical framework for understanding how both current environments and legacies of past selection shape human behavioral diversity. This integrative and pluralistic field, combining ethnographic, demographic, and sociological methods, has provided new insights into the ultimate forces and proximate pathways that guide human adaptation and variation. Here, we present the argument that evolutionary anthropological studies of human behavior also hold great, largely untapped, potential to guide the design, implementation, and evaluation of social and public health policy. Focusing on the key anthropological themes of reproduction, production, and distribution we highlight classic and recent research demonstrating the value of an evolutionary perspective to improving human well-being. The challenge now comes in transforming relevance into action and, for that, evolutionary behavioral anthropologists will need to forge deeper connections with other applied social scientists and policy-makers. We are hopeful that these developments are underway and that, with the current tide of enthusiasm for evidence-based approaches to policy, evolutionary anthropology is well positioned to make a strong contribution. PMID:25684561
NASA Astrophysics Data System (ADS)
Hibbard, Bill
2012-05-01
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
A framework for evolutionary systems biology
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
Ecological and evolutionary traps
Schlaepfer, Martin A.; Runge, M.C.; Sherman, P.W.
2002-01-01
Organisms often rely on environmental cues to make behavioral and life-history decisions. However, in environments that have been altered suddenly by humans, formerly reliable cues might no longer be associated with adaptive outcomes. In such cases, organisms can become 'trapped' by their evolutionary responses to the cues and experience reduced survival or reproduction. Ecological traps occur when organisms make poor habitat choices based on cues that correlated formerly with habitat quality. Ecological traps are part of a broader phenomenon, evolutionary traps, involving a dissociation between cues that organisms use to make any behavioral or life-history decision and outcomes normally associated with that decision. A trap can lead to extinction if a population falls below a critical size threshold before adaptation to the novel environment occurs. Conservation and management protocols must be designed in light of, rather than in spite of, the behavioral mechanisms and evolutionary history of populations and species to avoid 'trapping' them.
Evolutionary Debunking Arguments
Kahane, Guy
2011-01-01
Evolutionary debunking arguments (EDAs) are arguments that appeal to the evolutionary origins of evaluative beliefs to undermine their justification. This paper aims to clarify the premises and presuppositions of EDAs—a form of argument that is increasingly put to use in normative ethics. I argue that such arguments face serious obstacles. It is often overlooked, for example, that they presuppose the truth of metaethical objectivism. More importantly, even if objectivism is assumed, the use of EDAs in normative ethics is incompatible with a parallel and more sweeping global evolutionary debunking argument that has been discussed in recent metaethics. After examining several ways of responding to this global debunking argument, I end by arguing that even if we could resist it, this would still not rehabilitate the current targeted use of EDAs in normative ethics given that, if EDAs work at all, they will in any case lead to a truly radical revision of our evaluative outlook. PMID:21949447
Human nutrition: evolutionary perspectives.
Barnicot, N A
2005-01-01
In recent decades, much new evidence relating to the ape forerunners of modern humans has come to hand and diet appears to be an important factor. At some stage, there must have been a transition from a largely vegetarian ape diet to a modern human hunting economy providing significant amounts of meat. On an even longer evolutionary time scale the change was more complex. The mechanisms of evolutionary change are now better understood than they were in Darwin's time, thanks largely to great advances in genetics, both experimental and theoretical. It is virtually certain that diet, as a major component of the human environment, must have exerted evolutionary effects, but researchers still have little good evidence. PMID:17393680
A Model-Based Prognostics Approach Applied to Pneumatic Valves
NASA Technical Reports Server (NTRS)
Daigle, Matthew J.; Goebel, Kai
2011-01-01
Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
Investigating human evolutionary history
WOOD, BERNARD
2000-01-01
We rely on fossils for the interpretation of more than 95% of our evolutionary history. Fieldwork resulting in the recovery of fresh fossil evidence is an important component of reconstructing human evolutionary history, but advances can also be made by extracting additional evidence for the existing fossil record, and by improving the methods used to interpret the fossil evidence. This review shows how information from imaging and dental microstructure has contributed to improving our understanding of the hominin fossil record. It also surveys recent advances in the use of the fossil record for phylogenetic inference. PMID:10999269
The fastest evolutionary trajectory
Traulsen, Arne; Iwasa, Yoh; Nowak, Martin A.
2008-01-01
Given two mutants, A and B, separated by n mutational steps, what is the evolutionary trajectory which allows a homogeneous population of A to reach B in the shortest time? We show that the optimum evolutionary trajectory (fitness landscape) has the property that the relative fitness increase between any two consecutive steps is constant. Hence, the optimum fitness landscape between A and B is given by an exponential function. Our result is precise for small mutation rates and excluding back mutations. We discuss deviations for large mutation rates and including back mutations. For very large mutation rates, the optimum fitness landscape is flat and has a single peak at type B. PMID:17900629
Hybridization facilitates evolutionary rescue
Stelkens, Rike B; Brockhurst, Michael A; Hurst, Gregory D D; Greig, Duncan
2014-01-01
The resilience of populations to rapid environmental degradation is a major concern for biodiversity conservation. When environments deteriorate to lethal levels, species must evolve to adapt to the new conditions to avoid extinction. Here, we test the hypothesis that evolutionary rescue may be enabled by hybridization, because hybridization increases genetic variability. Using experimental evolution, we show that interspecific hybrid populations of Saccharomyces yeast adapt to grow in more highly degraded environments than intraspecific and parental crosses, resulting in survival rates far exceeding those of their ancestors. We conclude that hybridization can increase evolutionary responsiveness and that taxa able to exchange genes with distant relatives may better survive rapid environmental change. PMID:25558281
The Origins of Counting Algorithms
Cantlon, Jessica F.; Piantadosi, Steven T.; Ferrigno, Stephen; Hughes, Kelly D.; Barnard, Allison M.
2015-01-01
Humans’ ability to ‘count’ by verbally labeling discrete quantities is unique in animal cognition. The evolutionary origins of counting algorithms are not understood. We report that non-human primates exhibit a cognitive ability that is algorithmically and logically similar to human counting. Monkeys were given the task of choosing between two food caches. Monkeys saw one cache baited with some number of food items, one item at a time. Then, a second cache was baited with food items, one at a time. At the point when the second set approximately outnumbered the first set, monkeys spontaneously moved to choose the second set even before it was completely baited. Using a novel Bayesian analysis, we show that monkeys used an approximate counting algorithm to increment and compare quantities in sequence. This algorithm is structurally similar to formal counting in humans and thus may have been an important evolutionary precursor to human counting. PMID:25953949
Evolutionary Optimization of Yagi-Uda Antennas
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Colombano, Silvano P.
2001-01-01
Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.
Expediting model-based optoacoustic reconstructions with tomographic symmetries
Lutzweiler, Christian; Deán-Ben, Xosé Luís; Razansky, Daniel
2014-01-15
Purpose: Image quantification in optoacoustic tomography implies the use of accurate forward models of excitation, propagation, and detection of optoacoustic signals while inversions with high spatial resolution usually involve very large matrices, leading to unreasonably long computation times. The development of fast and memory efficient model-based approaches represents then an important challenge to advance on the quantitative and dynamic imaging capabilities of tomographic optoacoustic imaging. Methods: Herein, a method for simplification and acceleration of model-based inversions, relying on inherent symmetries present in common tomographic acquisition geometries, has been introduced. The method is showcased for the case of cylindrical symmetries by using polar image discretization of the time-domain optoacoustic forward model combined with efficient storage and inversion strategies. Results: The suggested methodology is shown to render fast and accurate model-based inversions in both numerical simulations andpost mortem small animal experiments. In case of a full-view detection scheme, the memory requirements are reduced by one order of magnitude while high-resolution reconstructions are achieved at video rate. Conclusions: By considering the rotational symmetry present in many tomographic optoacoustic imaging systems, the proposed methodology allows exploiting the advantages of model-based algorithms with feasible computational requirements and fast reconstruction times, so that its convenience and general applicability in optoacoustic imaging systems with tomographic symmetries is anticipated.
EVOLUTIONARY FOUNDATIONS FOR MOLECULAR MEDICINE
Nesse, Randolph M.; Ganten, Detlev; Gregory, T. Ryan; Omenn, Gilbert S.
2015-01-01
Evolution has long provided a foundation for population genetics, but many major advances in evolutionary biology from the 20th century are only now being applied in molecular medicine. They include the distinction between proximate and evolutionary explanations, kin selection, evolutionary models for cooperation, and new strategies for tracing phylogenies and identifying signals of selection. Recent advances in genomics are further transforming evolutionary biology and creating yet more opportunities for progress at the interface of evolution with genetics, medicine, and public health. This article reviews 15 evolutionary principles and their applications in molecular medicine in hopes that readers will use them and others to speed the development of evolutionary molecular medicine. PMID:22544168
Origins of evolutionary transitions.
Clarke, Ellen
2014-04-01
An 'evolutionary transition in individuality' or 'major transition' is a transformation in the hierarchical level at which natural selection operates on a population. In this article I give an abstract (i.e. level-neutral and substrate-neutral) articulation of the transition process in order to precisely understand how such processes can happen, especially how they can get started. PMID:24736161
Evolutionary Developmental Psychology.
ERIC Educational Resources Information Center
Geary, David C.; Bjorklund, David F.
2000-01-01
Describes evolutionary developmental psychology as the study of the genetic and ecological mechanisms that govern the development of social and cognitive competencies common to all human beings and the epigenetic (gene-environment interactions) processes that adapt these competencies to local conditions. Outlines basic assumptions and domains of…
Evolutionary developmental psychology.
King, Ashley C; Bjorklund, David F
2010-02-01
The field of evolutionary developmental psychology can potentially broaden the horizons of mainstream evolutionary psychology by combining the principles of Darwinian evolution by natural selection with the study of human development, focusing on the epigenetic effects that occur between humans and their environment in a way that attempts to explain how evolved psychological mechanisms become expressed in the phenotypes of adults. An evolutionary developmental perspective includes an appreciation of comparative research and we, among others, argue that contrasting the cognition of humans with that of nonhuman primates can provide a framework with which to understand how human cognitive abilities and intelligence evolved. Furthermore, we argue that several
Learning: An Evolutionary Analysis
ERIC Educational Resources Information Center
Swann, Joanna
2009-01-01
This paper draws on the philosophy of Karl Popper to present a descriptive evolutionary epistemology that offers philosophical solutions to the following related problems: "What happens when learning takes place?" and "What happens in human learning?" It provides a detailed analysis of how learning takes place without any direct transfer of…
Evolutionary Theories of Detection
Fitch, J P
2005-04-29
Current, mid-term and long range technologies for detection of pathogens and toxins are briefly described in the context of performance metrics and operational scenarios. Predictive (evolutionary) and speculative (revolutionary) assessments are given with trade-offs identified, where possible, among competing performance goals.
[Schizophrenia and evolutionary psychopathology].
Kelemen, Oguz; Kéri, Szabolcs
2007-01-01
Evolution can shape any characteristic appearing as a phenotype that is genetically rooted and possesses a long history. The stress-diathesis model suggests that psychiatric disorders have some genetic roots, and therefore the theory of evolution may be relevant for psychiatry. Schizophrenia is present in every human culture with approximately the same incidence. The great evolutionary paradox is: how can such illness persist despite fundamental reproductive disadvantages? Since the 1960s, several evolutionary explanations have been raised to explain the origins of schizophrenia. This article reviews all the major evolutionary theories about the possible origins of this disease. On the one hand, some researchers have proposed that schizophrenia is an evolutionary disadvantageous byproduct of human brain evolution (e.g. the evolution of hemispheric specialization, social brain or language skills). On the other hand, others have suggested that a compensatory advantage must exist either in the biological system of patients with schizophrenia (e.g. resistance against infectious diseases), or within the social domain (e.g. greater creativity of the relatives). According to some theories, shamanism and religion demonstrate some similarities to psychosis and provide clues regarding the origins of schizophrenia. At the end of this article we discuss this last theory in detail listing arguments for and against.
Evolutionary Theory under Fire.
ERIC Educational Resources Information Center
Lewin, Roger
1980-01-01
Summarizes events of a conference on evolutionary biology in Chicago entitled: "Macroevolution." Reviews the theory of modern synthesis, a term used to explain Darwinism in terms of population biology and genetics. Issues presented at the conference are discussed in detail. (CS)
3-D model-based tracking for UAV indoor localization.
Teulière, Céline; Marchand, Eric; Eck, Laurent
2015-05-01
This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights.
3-D model-based tracking for UAV indoor localization.
Teulière, Céline; Marchand, Eric; Eck, Laurent
2015-05-01
This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights. PMID:25099967
Hybrid and adaptive meta-model-based global optimization
NASA Astrophysics Data System (ADS)
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Zhou, Yongquan; Xie, Jian; Li, Liangliang; Ma, Mingzhi
2014-01-01
Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new cloud model bat algorithm (CBA) is proposed. This paper focuses on remodeling echolocation model based on living and preying characteristics of bats, utilizing the transformation theory of cloud model to depict the qualitative concept: "bats approach their prey." Furthermore, Lévy flight mode and population information communication mechanism of bats are introduced to balance the advantage between exploration and exploitation. The simulation results show that the cloud model bat algorithm has good performance on functions optimization. PMID:24967425
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.
Model-based reconstruction for x-ray diffraction imaging
NASA Astrophysics Data System (ADS)
Sridhar, Venkatesh; Kisner, Sherman J.; Skatter, Sondre; Bouman, Charles A.
2016-05-01
In this paper, we propose a novel 4D model-based iterative reconstruction (MBIR) algorithm for low-angle scatter X-ray Diffraction (XRD) that can substantially increase the SNR. Our forward model is based on a Poisson photon counting model that incorporates a spatial point-spread function, detector energy response and energy-dependent attenuation correction. Our prior model uses a Markov random field (MRF) together with a reduced spectral bases set determined using non-negative matrix factorization. We demonstrate the effectiveness of our method with real data sets.
A model-based multisensor data fusion knowledge management approach
NASA Astrophysics Data System (ADS)
Straub, Jeremy
2014-06-01
A variety of approaches exist for combining data from multiple sensors. The model-based approach combines data based on its support for or refutation of elements of the model which in turn can be used to evaluate an experimental thesis. This paper presents a collection of algorithms for mapping various types of sensor data onto a thesis-based model and evaluating the truth or falsity of the thesis, based on the model. The use of this approach for autonomously arriving at findings and for prioritizing data are considered. Techniques for updating the model (instead of arriving at a true/false assertion) are also discussed.
Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis
NASA Astrophysics Data System (ADS)
Fu, Pei-hua; Yin, Hong-bo
In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.
Analysis of Massive Emigration from Poland: The Model-Based Clustering Approach
NASA Astrophysics Data System (ADS)
Witek, Ewa
The model-based approach assumes that data is generated by a finite mixture of probability distributions such as multivariate normal distributions. In finite mixture models, each component of probability distribution corresponds to a cluster. The problem of determining the number of clusters and choosing an appropriate clustering method becomes the problem of statistical model choice. Hence, the model-based approach provides a key advantage over heuristic clustering algorithms, because it selects both the correct model and the number of clusters.
Evolutionary Computational Methods for Identifying Emergent Behavior in Autonomous Systems
NASA Technical Reports Server (NTRS)
Terrile, Richard J.; Guillaume, Alexandre
2011-01-01
A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum. Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.
Evolutionary biology of cancer.
Crespi, Bernard; Summers, Kyle
2005-10-01
Cancer is driven by the somatic evolution of cell lineages that have escaped controls on replication and by the population-level evolution of genes that influence cancer risk. We describe here how recent evolutionary ecological studies have elucidated the roles of predation by the immune system and competition among normal and cancerous cells in the somatic evolution of cancer. Recent analyses of the evolution of cancer at the population level show how rapid changes in human environments have augmented cancer risk, how strong selection has frequently led to increased cancer risk as a byproduct, and how anticancer selection has led to tumor-suppression systems, tissue designs that slow somatic evolution, constraints on morphological evolution and even senescence itself. We discuss how applications of the tools of ecology and evolutionary biology are poised to revolutionize our understanding and treatment of this disease.
Evolutionary Determinants of Cancer
Greaves, Mel
2015-01-01
‘Nothing in biology makes sense except in the light of evolution’ Th. Dobzhansky, 1973 Our understanding of cancer is being transformed by exploring clonal diversity, drug resistance and causation within an evolutionary framework. The therapeutic resilience of advanced cancer is a consequence of its character as complex, dynamic and adaptive ecosystem engendering robustness, underpinned by genetic diversity and epigenetic plasticity. The risk of mutation-driven escape by self-renewing cells is intrinsic to multicellularity but is countered by multiple restraints facilitating increasing complexity and longevity of species. But our own has disrupted this historical narrative by rapidly escalating intrinsic risk. Evolutionary principles illuminate these challenges and provide new avenues to explore for more effective control. PMID:26193902
NASA Astrophysics Data System (ADS)
Smith, John Maynard
1986-10-01
It is often the case that the best thing for an animal or plant to do depends on what other members of the population are doing. In more technical language, the fitnesses of different phenotypes are frequency-dependent. Evolutionary game theory has been developed to analyse such cases. In this paper I aim to do three things. First, I describe the concepts of evolutionary game theory in the context of a simple game, the Hawk-Dove game, and compare them with the concepts of classical game theory originating with Von Neumann and Morgenstern (1953) [1]. Second, I list some of the applications of the theory. Finally, I suggest how the theory can tell us something about the evolution of learning.
Hybrid multiobjective evolutionary design for artificial neural networks.
Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen
2008-09-01
Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.
Adaptive noise cancellation based on beehive pattern evolutionary digital filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin
2014-01-01
Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.
Qualitative model-based diagnostics for rocket systems
NASA Technical Reports Server (NTRS)
Maul, William; Meyer, Claudia; Jankovsky, Amy; Fulton, Christopher
1993-01-01
A diagnostic software package is currently being developed at NASA LeRC that utilizes qualitative model-based reasoning techniques. These techniques can provide diagnostic information about the operational condition of the modeled rocket engine system or subsystem. The diagnostic package combines a qualitative model solver with a constraint suspension algorithm. The constraint suspension algorithm directs the solver's operation to provide valuable fault isolation information about the modeled system. A qualitative model of the Space Shuttle Main Engine's oxidizer supply components was generated. A diagnostic application based on this qualitative model was constructed to process four test cases: three numerical simulations and one actual test firing. The diagnostic tool's fault isolation output compared favorably with the input fault condition.
Predicting evolutionary dynamics
NASA Astrophysics Data System (ADS)
Balazsi, Gabor
We developed an ordinary differential equation-based model to predict the evolutionary dynamics of yeast cells carrying a synthetic gene circuit. The predicted aspects included the speed at which the ancestral genotype disappears from the population; as well as the types of mutant alleles that establish in each environmental condition. We validated these predictions by experimental evolution. The agreement between our predictions and experimental findings suggests that cellular and population fitness landscapes can be useful to predict short-term evolution.
Evolutionary theory of cancer.
Attolini, Camille Stephan-Otto; Michor, Franziska
2009-06-01
As Theodosius Dobzhansky famously noted in 1973, "Nothing in biology makes sense except in the light of evolution," and cancer is no exception to this rule. Our understanding of cancer initiation, progression, treatment, and resistance has advanced considerably by regarding cancer as the product of evolutionary processes. Here we review the literature of mathematical models of cancer evolution and provide a synthesis and discussion of the field.
Evidence for model-based computations in the human amygdala during Pavlovian conditioning.
Prévost, Charlotte; McNamee, Daniel; Jessup, Ryan K; Bossaerts, Peter; O'Doherty, John P
2013-01-01
Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference.
Practical Application of Model-based Programming and State-based Architecture to Space Missions
NASA Technical Reports Server (NTRS)
Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian
2006-01-01
A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps
Evolutionary theory and teleology.
O'Grady, R T
1984-04-21
The order within and among living systems can be explained rationally by postulating a process of descent with modification, effected by factors which are extrinsic or intrinsic to the organisms. Because at the time Darwin proposed his theory of evolution there was no concept of intrinsic factors which could evolve, he postulated a process of extrinsic effects--natural selection. Biological order was thus seen as an imposed, rather than an emergent, property. Evolutionary change was seen as being determined by the functional efficiency (adaptedness) of the organism in its environment, rather than by spontaneous changes in intrinsically generated organizing factors. The initial incompleteness of Darwin's explanatory model, and the axiomatization of its postulates in neo-Darwinism, has resulted in a theory of functionalism, rather than structuralism. As such, it introduces an unnecessary teleology which confounds evolutionary studies and reduces the usefulness of the theory. This problem cannot be detected from within the neo-Darwinian paradigm because the different levels of end-directed activity--teleomatic, teleonomic, and teleological--are not recognized. They are, in fact, considered to influence one another. The theory of nonequilibrium evolution avoids these problems by returning to the basic principles of biological order and developing a structuralist explanation of intrinsically generated change. Extrinsic factors may affect the resultant evolutionary pattern, but they are neither necessary nor sufficient for evolution to occur.
Evolutionary mysteries in meiosis.
Lenormand, Thomas; Engelstädter, Jan; Johnston, Susan E; Wijnker, Erik; Haag, Christoph R
2016-10-19
Meiosis is a key event of sexual life cycles in eukaryotes. Its mechanistic details have been uncovered in several model organisms, and most of its essential features have received various and often contradictory evolutionary interpretations. In this perspective, we present an overview of these often 'weird' features. We discuss the origin of meiosis (origin of ploidy reduction and recombination, two-step meiosis), its secondary modifications (in polyploids or asexuals, inverted meiosis), its importance in punctuating life cycles (meiotic arrests, epigenetic resetting, meiotic asymmetry, meiotic fairness) and features associated with recombination (disjunction constraints, heterochiasmy, crossover interference and hotspots). We present the various evolutionary scenarios and selective pressures that have been proposed to account for these features, and we highlight that their evolutionary significance often remains largely mysterious. Resolving these mysteries will likely provide decisive steps towards understanding why sex and recombination are found in the majority of eukaryotes.This article is part of the themed issue 'Weird sex: the underappreciated diversity of sexual reproduction'.
McAvoy, Alex; Hauert, Christoph
2015-01-01
Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals. A common assumption in evolutionary game theory is that interactions are symmetric, which means that the players are distinguished by only their strategies. In nature, however, the microscopic interactions between players are nearly always asymmetric due to environmental effects, differing baseline characteristics, and other possible sources of heterogeneity. To model these phenomena, we introduce into evolutionary game theory two broad classes of asymmetric interactions: ecological and genotypic. Ecological asymmetry results from variation in the environments of the players, while genotypic asymmetry is a consequence of the players having differing baseline genotypes. We develop a theory of these forms of asymmetry for games in structured populations and use the classical social dilemmas, the Prisoner’s Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric games reveal essential differences between models of genetic evolution based on reproduction and models of cultural evolution based on imitation that are not apparent in symmetric games. PMID:26308326
Evolutionary consequences of autopolyploidy.
Parisod, Christian; Holderegger, Rolf; Brochmann, Christian
2010-04-01
Autopolyploidy is more common in plants than traditionally assumed, but has received little attention compared with allopolyploidy. Hence, the advantages and disadvantages of genome doubling per se compared with genome doubling coupled with hybridizations in allopolyploids remain unclear. Autopolyploids are characterized by genomic redundancy and polysomic inheritance, increasing effective population size. To shed light on the evolutionary consequences of autopolyploidy, we review a broad range of studies focusing on both synthetic and natural autopolyploids encompassing levels of biological organization from genes to evolutionary lineages. The limited evidence currently available suggests that autopolyploids neither experience strong genome restructuring nor wide reorganization of gene expression during the first generations following genome doubling, but that these processes may become more important in the longer term. Biogeographic and ecological surveys point to an association between the formation of autopolyploid lineages and environmental change. We thus hypothesize that polysomic inheritance may provide a short-term evolutionary advantage for autopolyploids compared to diploid relatives when environmental change enforces range shifts. In addition, autopolyploids should possess increased genome flexibility, allowing them to adapt and persist across heterogeneous landscapes in the long run. PMID:20070540
Evolutionary mysteries in meiosis.
Lenormand, Thomas; Engelstädter, Jan; Johnston, Susan E; Wijnker, Erik; Haag, Christoph R
2016-10-19
Meiosis is a key event of sexual life cycles in eukaryotes. Its mechanistic details have been uncovered in several model organisms, and most of its essential features have received various and often contradictory evolutionary interpretations. In this perspective, we present an overview of these often 'weird' features. We discuss the origin of meiosis (origin of ploidy reduction and recombination, two-step meiosis), its secondary modifications (in polyploids or asexuals, inverted meiosis), its importance in punctuating life cycles (meiotic arrests, epigenetic resetting, meiotic asymmetry, meiotic fairness) and features associated with recombination (disjunction constraints, heterochiasmy, crossover interference and hotspots). We present the various evolutionary scenarios and selective pressures that have been proposed to account for these features, and we highlight that their evolutionary significance often remains largely mysterious. Resolving these mysteries will likely provide decisive steps towards understanding why sex and recombination are found in the majority of eukaryotes.This article is part of the themed issue 'Weird sex: the underappreciated diversity of sexual reproduction'. PMID:27619705
Martinez-Vaquero, Luis A; Cuesta, José A
2013-05-01
Indirect reciprocity is one of the main mechanisms to explain the emergence and sustainment of altruism in societies. The standard approach to indirect reciprocity is reputation models. These are games in which players base their decisions on their opponent's reputation gained in past interactions with other players (moral assessment). The combination of actions and moral assessment leads to a large diversity of strategies; thus determining the stability of any of them against invasions by all the others is a difficult task. We use a variant of a previously introduced reputation-based model that let us systematically analyze all these invasions and determine which ones are successful. Accordingly, we are able to identify the third-order strategies (those which, apart from the action, judge considering both the reputation of the donor and that of the recipient) that are evolutionarily stable. Our results reveal that if a strategy resists the invasion of any other one sharing its same moral assessment, it can resist the invasion of any other strategy. However, if actions are not always witnessed, cheaters (i.e., individuals with a probability of defecting regardless of the opponent's reputation) have a chance to defeat the stable strategies for some choices of the probabilities of cheating and of being witnessed. Remarkably, by analyzing this issue with adaptive dynamics we find that whether an honest population resists the invasion of cheaters is determined by a Hamilton-like rule, with the probability that the cheat is discovered playing the role of the relatedness parameter.
NASA Astrophysics Data System (ADS)
Martinez-Vaquero, Luis A.; Cuesta, José A.
2013-05-01
Indirect reciprocity is one of the main mechanisms to explain the emergence and sustainment of altruism in societies. The standard approach to indirect reciprocity is reputation models. These are games in which players base their decisions on their opponent's reputation gained in past interactions with other players (moral assessment). The combination of actions and moral assessment leads to a large diversity of strategies; thus determining the stability of any of them against invasions by all the others is a difficult task. We use a variant of a previously introduced reputation-based model that let us systematically analyze all these invasions and determine which ones are successful. Accordingly, we are able to identify the third-order strategies (those which, apart from the action, judge considering both the reputation of the donor and that of the recipient) that are evolutionarily stable. Our results reveal that if a strategy resists the invasion of any other one sharing its same moral assessment, it can resist the invasion of any other strategy. However, if actions are not always witnessed, cheaters (i.e., individuals with a probability of defecting regardless of the opponent's reputation) have a chance to defeat the stable strategies for some choices of the probabilities of cheating and of being witnessed. Remarkably, by analyzing this issue with adaptive dynamics we find that whether an honest population resists the invasion of cheaters is determined by a Hamilton-like rule, with the probability that the cheat is discovered playing the role of the relatedness parameter.
Multiple Damage Progression Paths in Model-Based Prognostics
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Goebel, Kai Frank
2011-01-01
Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active
Topological evolutionary computing in the optimal design of 2D and 3D structures
NASA Astrophysics Data System (ADS)
Burczynski, T.; Poteralski, A.; Szczepanik, M.
2007-10-01
An application of evolutionary algorithms and the finite-element method to the topology optimization of 2D structures (plane stress, bending plates, and shells) and 3D structures is described. The basis of the topological evolutionary optimization is the direct control of the density material distribution (or thickness for 2D structures) by the evolutionary algorithm. The structures are optimized for stress, mass, and compliance criteria. The numerical examples demonstrate that this method is an effective technique for solving problems in computer-aided optimal design.
Adaptive evolutionary artificial neural networks for pattern classification.
Oong, Tatt Hee; Isa, Nor Ashidi Mat
2011-11-01
This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms. PMID:21968733
Kitaev models based on unitary quantum groupoids
Chang, Liang
2014-04-15
We establish a generalization of Kitaev models based on unitary quantum groupoids. In particular, when inputting a Kitaev-Kong quantum groupoid H{sub C}, we show that the ground state manifold of the generalized model is canonically isomorphic to that of the Levin-Wen model based on a unitary fusion category C. Therefore, the generalized Kitaev models provide realizations of the target space of the Turaev-Viro topological quantum field theory based on C.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Transition matrix model for evolutionary game dynamics.
Ermentrout, G Bard; Griffin, Christopher; Belmonte, Andrew
2016-03-01
We study an evolutionary game model based on a transition matrix approach, in which the total change in the proportion of a population playing a given strategy is summed directly over contributions from all other strategies. This general approach combines aspects of the traditional replicator model, such as preserving unpopulated strategies, with mutation-type dynamics, which allow for nonzero switching to unpopulated strategies, in terms of a single transition function. Under certain conditions, this model yields an endemic population playing non-Nash-equilibrium strategies. In addition, a Hopf bifurcation with a limit cycle may occur in the generalized rock-scissors-paper game, unlike the replicator equation. Nonetheless, many of the Folk Theorem results are shown to hold for this model. PMID:27078323
Transition matrix model for evolutionary game dynamics
NASA Astrophysics Data System (ADS)
Ermentrout, G. Bard; Griffin, Christopher; Belmonte, Andrew
2016-03-01
We study an evolutionary game model based on a transition matrix approach, in which the total change in the proportion of a population playing a given strategy is summed directly over contributions from all other strategies. This general approach combines aspects of the traditional replicator model, such as preserving unpopulated strategies, with mutation-type dynamics, which allow for nonzero switching to unpopulated strategies, in terms of a single transition function. Under certain conditions, this model yields an endemic population playing non-Nash-equilibrium strategies. In addition, a Hopf bifurcation with a limit cycle may occur in the generalized rock-scissors-paper game, unlike the replicator equation. Nonetheless, many of the Folk Theorem results are shown to hold for this model.
Transition matrix model for evolutionary game dynamics.
Ermentrout, G Bard; Griffin, Christopher; Belmonte, Andrew
2016-03-01
We study an evolutionary game model based on a transition matrix approach, in which the total change in the proportion of a population playing a given strategy is summed directly over contributions from all other strategies. This general approach combines aspects of the traditional replicator model, such as preserving unpopulated strategies, with mutation-type dynamics, which allow for nonzero switching to unpopulated strategies, in terms of a single transition function. Under certain conditions, this model yields an endemic population playing non-Nash-equilibrium strategies. In addition, a Hopf bifurcation with a limit cycle may occur in the generalized rock-scissors-paper game, unlike the replicator equation. Nonetheless, many of the Folk Theorem results are shown to hold for this model.
Optimizing a reconfigurable material via evolutionary computation
NASA Astrophysics Data System (ADS)
Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.
2015-08-01
Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.
Optimizing a reconfigurable material via evolutionary computation.
Wilken, Sam; Miskin, Marc Z; Jaeger, Heinrich M
2015-08-01
Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 10^{10} possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions. PMID:26382399
Evolutionary Computing for Low-thrust Navigation
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Fink, Wolfgang; vonAllmed, Paul; Petropoulos, Anastassios E.; Russell, Ryan P.; Terrile, Richard J.
2005-01-01
The development of new mission concepts requires efficient methodologies to analyze, design and simulate the concepts before implementation. New mission concepts are increasingly considering the use of ion thrusters for fuel-efficient navigation in deep space. This paper presents parallel, evolutionary computing methods to design trajectories of spacecraft propelled by ion thrusters and to assess the trade-off between delivered payload mass and required flight time. The developed methods utilize a distributed computing environment in order to speed up computation, and use evolutionary algorithms to find globally Pareto-optimal solutions. The methods are coupled with two main traditional trajectory design approaches, which are called direct and indirect. In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. In the indirect approach, a thrust control problem is transformed into a costate control problem, and the initial values of the costate vector are optimized. The developed methods are applied to two problems: 1) an orbit transfer around the Earth and 2) a transfer between two distance retrograde orbits around Europa, the closest to Jupiter of the icy Galilean moons. The optimal solutions found with the present methods are comparable to other state-of-the-art trajectory optimizers and to analytical approximations for optimal transfers, while the required computational time is several orders of magnitude shorter than other optimizers thanks to an intelligent design of control vector discretization, advanced algorithmic parameterization, and parallel computing.
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.
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. PMID:3689299
Evolutionary models of binaries
NASA Astrophysics Data System (ADS)
van Rensbergen, Walter; Mennekens, Nicki; de Greve, Jean-Pierre; Jansen, Kim; de Loore, Bert
2011-07-01
We have put on CDS a catalog containing 561 evolutionary models of binaries: J/A+A/487/1129 (Van Rensbergen+, 2008). The catalog covers a grid of binaries with a B-type primary at birth, different values for the initial mass ratio and a wide range of initial orbital periods. The evolution was calculated with the Brussels code in which we introduced the spinning up and the creation of a hot spot on the gainer or its accretion disk, caused by impacting mass coming from the donor. When the kinetic energy of fast rotation added to the radiative energy of the hot spot exceeds the binding energy, a fraction of the transferred matter leaves the system: the evolution is liberal during a short lasting era of rapid mass transfer. The spin-up of the gainer was modulated using both strong and weak tides. The catalog shows the results for both types. For comparison, we included the evolutionary tracks calculated with the conservative assumption. Binaries with an initial primary below 6 Msolar show hardly any mass loss from the system and thus evolve conservatively. Above this limit differences between liberal and conservative evolution grow with increasing initial mass of the primary star.
Evolutionary status of Polaris
NASA Astrophysics Data System (ADS)
Fadeyev, Yu. A.
2015-05-01
Hydrodynamic models of short-period Cepheids were computed to determine the pulsation period as a function of evolutionary time during the first and third crossings of the instability strip. The equations of radiation hydrodynamics and turbulent convection for radial stellar pulsations were solved with the initial conditions obtained from the evolutionary models of Population I stars (X = 0.7, Z = 0.02) with masses from 5.2 to 6.5 M⊙ and the convective core overshooting parameter 0.1 ≤ αov ≤ 0.3. In Cepheids with period of 4 d the rate of pulsation period change during the first crossing of the instability strip is over 50 times larger than that during the third crossing. Polaris is shown to cross the instability strip for the first time and to be the fundamental mode pulsator. The best agreement between the predicted and observed rates of period change was obtained for the model with mass of 5.4 M⊙ and the overshooting parameter αov = 0.25. The bolometric luminosity and radius are L = 1.26 × 103 L⊙ and R = 37.5 R⊙, respectively. In the HR diagram, Polaris is located at the red edge of the instability strip.
A constraint consensus memetic algorithm for solving constrained optimization problems
NASA Astrophysics Data System (ADS)
Hamza, Noha M.; Sarker, Ruhul A.; Essam, Daryl L.; Deb, Kalyanmoy; Elsayed, Saber M.
2014-11-01
Constraint handling is an important aspect of evolutionary constrained optimization. Currently, the mechanism used for constraint handling with evolutionary algorithms mainly assists the selection process, but not the actual search process. In this article, first a genetic algorithm is combined with a class of search methods, known as constraint consensus methods, that assist infeasible individuals to move towards the feasible region. This approach is also integrated with a memetic algorithm. The proposed algorithm is tested and analysed by solving two sets of standard benchmark problems, and the results are compared with other state-of-the-art algorithms. The comparisons show that the proposed algorithm outperforms other similar algorithms. The algorithm has also been applied to solve a practical economic load dispatch problem, where it also shows superior performance over other algorithms.
A conceptual evolutionary aseismic decision support framework for hospitals
NASA Astrophysics Data System (ADS)
Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun
2012-12-01
In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.
Emergence of structured communities through evolutionary dynamics.
Shtilerman, Elad; Kessler, David A; Shnerb, Nadav M
2015-10-21
Species-rich communities, in which many competing species coexist in a single trophic level, are quite frequent in nature, but pose a formidable theoretical challenge. In particular, it is known that complex competitive systems become unstable and unfeasible when the number of species is large. Recently, many studies have attributed the stability of natural communities to the structure of the interspecific interaction network, yet the nature of such structures and the underlying mechanisms responsible for them remain open questions. Here we introduce an evolutionary model, based on the generic Lotka-Volterra competitive framework, from which a stable, structured, diverse community emerges spontaneously. The modular structure of the competition matrix reflects the phylogeny of the community, in agreement with the hierarchial taxonomic classification. Closely related species tend to have stronger niche overlap and weaker fitness differences, as opposed to pairs of species from different modules. The competitive-relatedness hypothesis and the idea of emergent neutrality are discussed in the context of this evolutionary model.
Emergence of structured communities through evolutionary dynamics.
Shtilerman, Elad; Kessler, David A; Shnerb, Nadav M
2015-10-21
Species-rich communities, in which many competing species coexist in a single trophic level, are quite frequent in nature, but pose a formidable theoretical challenge. In particular, it is known that complex competitive systems become unstable and unfeasible when the number of species is large. Recently, many studies have attributed the stability of natural communities to the structure of the interspecific interaction network, yet the nature of such structures and the underlying mechanisms responsible for them remain open questions. Here we introduce an evolutionary model, based on the generic Lotka-Volterra competitive framework, from which a stable, structured, diverse community emerges spontaneously. The modular structure of the competition matrix reflects the phylogeny of the community, in agreement with the hierarchial taxonomic classification. Closely related species tend to have stronger niche overlap and weaker fitness differences, as opposed to pairs of species from different modules. The competitive-relatedness hypothesis and the idea of emergent neutrality are discussed in the context of this evolutionary model. PMID:26231415
Model-Based Diagnosis in a Power Distribution Test-Bed
NASA Technical Reports Server (NTRS)
Scarl, E.; McCall, K.
1998-01-01
The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.
NASA Technical Reports Server (NTRS)
Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.
2015-01-01
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
Electrochemical model based charge optimization for lithium-ion batteries
NASA Astrophysics Data System (ADS)
Pramanik, Sourav; Anwar, Sohel
2016-05-01
In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.
Evolutionary Design of Controlled Structures
NASA Technical Reports Server (NTRS)
Masters, Brett P.; Crawley, Edward F.
1997-01-01
Basic physical concepts of structural delay and transmissibility are provided for simple rod and beam structures. Investigations show the sensitivity of these concepts to differing controlled-structures variables, and to rational system modeling effects. An evolutionary controls/structures design method is developed. The basis of the method is an accurate model formulation for dynamic compensator optimization and Genetic Algorithm based updating of sensor/actuator placement and structural attributes. One and three dimensional examples from the literature are used to validate the method. Frequency domain interpretation of these controlled structure systems provide physical insight as to how the objective is optimized and consequently what is important in the objective. Several disturbance rejection type controls-structures systems are optimized for a stellar interferometer spacecraft application. The interferometric designs include closed loop tracking optics. Designs are generated for differing structural aspect ratios, differing disturbance attributes, and differing sensor selections. Physical limitations in achieving performance are given in terms of average system transfer function gains and system phase loss. A spacecraft-like optical interferometry system is investigated experimentally over several different optimized controlled structures configurations. Configurations represent common and not-so-common approaches to mitigating pathlength errors induced by disturbances of two different spectra. Results show that an optimized controlled structure for low frequency broadband disturbances achieves modest performance gains over a mass equivalent regular structure, while an optimized structure for high frequency narrow band disturbances is four times better in terms of root-mean-square pathlength. These results are predictable given the nature of the physical system and the optimization design variables. Fundamental limits on controlled performance are discussed
NASA Astrophysics Data System (ADS)
Hortos, William S.
2011-06-01
Published studies have focused on the application of one bio-inspired or evolutionary computational method to the functions of a single protocol layer in a wireless ad hoc sensor network (WSN). For example, swarm intelligence in the form of ant colony optimization (ACO), has been repeatedly considered for the routing of data/information among nodes, a network-layer function, while genetic algorithms (GAs) have been used to select transmission frequencies and power levels, physical-layer functions. Similarly, artificial immune systems (AISs) as well as trust models of quantized data reputation have been invoked for detection of network intrusions that cause anomalies in data and information; these act on the application and presentation layers. Most recently, a self-organizing scheduling scheme inspired by frog-calling behavior for reliable data transmission in wireless sensor networks, termed anti-phase synchronization, has been applied to realize collision-free transmissions between neighboring nodes, a function of the MAC layer. In a novel departure from previous work, the cross-layer approach to WSN protocol design suggests applying more than one evolutionary computational method to the functions of the appropriate layers to improve the QoS performance of the cross-layer design beyond that of one method applied to a single layer's functions. A baseline WSN protocol design, embedding GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layers, respectively, is constructed. Simulation results demonstrate the synergies among the bioinspired/ evolutionary methods of the proposed baseline design improve the overall QoS performance of networks over that of a single computational method.
Spore: Spawning Evolutionary Misconceptions?
NASA Astrophysics Data System (ADS)
Bean, Thomas E.; Sinatra, Gale M.; Schrader, P. G.
2010-10-01
The use of computer simulations as educational tools may afford the means to develop understanding of evolution as a natural, emergent, and decentralized process. However, special consideration of developmental constraints on learning may be necessary when using these technologies. Specifically, the essentialist (biological forms possess an immutable essence), teleological (assignment of purpose to living things and/or parts of living things that may not be purposeful), and intentionality (assumption that events are caused by an intelligent agent) biases may be reinforced through the use of computer simulations, rather than addressed with instruction. We examine the video game Spore for its depiction of evolutionary content and its potential to reinforce these cognitive biases. In particular, we discuss three pedagogical strategies to mitigate weaknesses of Spore and other computer simulations: directly targeting misconceptions through refutational approaches, targeting specific principles of scientific inquiry, and directly addressing issues related to models as cognitive tools.
Model-based fault detection and identification with online aerodynamic model structure selection
NASA Astrophysics Data System (ADS)
Lombaerts, T.
2013-12-01
This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modification of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identification (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on a simulation data set.
Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR
NASA Astrophysics Data System (ADS)
Li, Baoxin; Zheng, Qinfen; Der, Sandor Z.; Chellappa, Rama; Nasrabadi, Nasser M.; Chan, Lipchen A.; Wang, LinCheng
1998-09-01
This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking InfraRed (FLIR) imagery using a large database of real second-generation FLIR images. The algorithms evaluated are based on convolution neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), and modular neural networks (MNN). Two model-based algorithms, using Hausdorff metric based matching and geometric hashing, are also evaluated. A hierarchial pose estimation system using CNN plus either PCA or LDA, developed by the authors, is also evaluated using the same data set.
Child Development and Evolutionary Psychology.
ERIC Educational Resources Information Center
Bjorklund, David F.; Pellegrini, Anthony D.
2000-01-01
Argues that an evolutionary account provides insight into developmental function and individual differences. Outlines some assumptions of evolutionary psychology related to development. Introduces the developmental systems approach, differential influence of natural selection at different points in ontogeny, and development of evolved…
Open Issues in Evolutionary Robotics.
Silva, Fernando; Duarte, Miguel; Correia, Luís; Oliveira, Sancho Moura; Christensen, Anders Lyhne
2016-01-01
One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.
Observability in dynamic evolutionary models.
López, I; Gámez, M; Carreño, R
2004-02-01
In the paper observability problems are considered in basic dynamic evolutionary models for sexual and asexual populations. Observability means that from the (partial) knowledge of certain phenotypic characteristics the whole evolutionary process can be uniquely recovered. Sufficient conditions are given to guarantee observability for both sexual and asexual populations near an evolutionarily stable state.
Testing Strategies for Model-Based Development
NASA Technical Reports Server (NTRS)
Heimdahl, Mats P. E.; Whalen, Mike; Rajan, Ajitha; Miller, Steven P.
2006-01-01
This report presents an approach for testing artifacts generated in a model-based development process. This approach divides the traditional testing process into two parts: requirements-based testing (validation testing) which determines whether the model implements the high-level requirements and model-based testing (conformance testing) which determines whether the code generated from a model is behaviorally equivalent to the model. The goals of the two processes differ significantly and this report explores suitable testing metrics and automation strategies for each. To support requirements-based testing, we define novel objective requirements coverage metrics similar to existing specification and code coverage metrics. For model-based testing, we briefly describe automation strategies and examine the fault-finding capability of different structural coverage metrics using tests automatically generated from the model.
Model-Based Prognostics of Hybrid Systems
NASA Technical Reports Server (NTRS)
Daigle, Matthew; Roychoudhury, Indranil; Bregon, Anibal
2015-01-01
Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks
NASA Astrophysics Data System (ADS)
Lo, Chun; Lynch, Jerome P.; Liu, Mingyan
2016-01-01
Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.
How competition affects evolutionary rescue
Osmond, Matthew Miles; de Mazancourt, Claire
2013-01-01
Populations facing novel environments can persist by adapting. In nature, the ability to adapt and persist will depend on interactions between coexisting individuals. Here we use an adaptive dynamic model to assess how the potential for evolutionary rescue is affected by intra- and interspecific competition. Intraspecific competition (negative density-dependence) lowers abundance, which decreases the supply rate of beneficial mutations, hindering evolutionary rescue. On the other hand, interspecific competition can aid evolutionary rescue when it speeds adaptation by increasing the strength of selection. Our results clarify this point and give an additional requirement: competition must increase selection pressure enough to overcome the negative effect of reduced abundance. We therefore expect evolutionary rescue to be most likely in communities which facilitate rapid niche displacement. Our model, which aligns to previous quantitative and population genetic models in the absence of competition, provides a first analysis of when competitors should help or hinder evolutionary rescue. PMID:23209167
Multimode model based defect characterization in composites
NASA Astrophysics Data System (ADS)
Roberts, R.; Holland, S.; Gregory, E.
2016-02-01
A newly-initiated research program for model-based defect characterization in CFRP composites is summarized. The work utilizes computational models of the interaction of NDE probing energy fields (ultrasound and thermography), to determine 1) the measured signal dependence on material and defect properties (forward problem), and 2) an assessment of performance-critical defect properties from analysis of measured NDE signals (inverse problem). Work is reported on model implementation for inspection of CFRP laminates containing delamination and porosity. Forward predictions of measurement response are presented, as well as examples of model-based inversion of measured data for the estimation of defect parameters.
Model-based internal wave processing
Candy, J.V.; Chambers, D.H.
1995-06-09
A model-based approach is proposed to solve the oceanic internal wave signal processing problem that is based on state-space representations of the normal-mode vertical velocity and plane wave horizontal velocity propagation models. It is shown that these representations can be utilized to spatially propagate the modal (dept) vertical velocity functions given the basic parameters (wave numbers, Brunt-Vaisala frequency profile etc.) developed from the solution of the associated boundary value problem as well as the horizontal velocity components. Based on this framework, investigations are made of model-based solutions to the signal enhancement problem for internal waves.
A Breeder Algorithm for Stellarator Optimization
NASA Astrophysics Data System (ADS)
Wang, S.; Ware, A. S.; Hirshman, S. P.; Spong, D. A.
2003-10-01
An optimization algorithm that combines the global parameter space search properties of a genetic algorithm (GA) with the local parameter search properties of a Levenberg-Marquardt (LM) algorithm is described. Optimization algorithms used in the design of stellarator configurations are often classified as either global (such as GA and differential evolution algorithm) or local (such as LM). While nonlinear least-squares methods such as LM are effective at minimizing a cost-function based on desirable plasma properties such as quasi-symmetry and ballooning stability, whether or not this is a local or global minimum is unknown. The advantage of evolutionary algorithms such as GA is that they search a wider range of parameter space and are not susceptible to getting stuck in a local minimum of the cost function. Their disadvantage is that in some cases the evolutionary algorithms are ineffective at finding a minimum state. Here, we describe the initial development of the Breeder Algorithm (BA). BA consists of a genetic algorithm outer loop with an inner loop in which each generation is refined using a LM step. Initial results for a quasi-poloidal stellarator optimization will be presented, along with a comparison to existing optimization algorithms.
On the Performance of Stochastic Model-Based Image Segmentation
NASA Astrophysics Data System (ADS)
Lei, Tianhu; Sewchand, Wilfred
1989-11-01
A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].
System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft
NASA Technical Reports Server (NTRS)
Pullen, Samuel P.; Parkinson, Bradford W.
1994-01-01
This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.
RNA based evolutionary optimization.
Schuster, P
1993-12-01
The notion of an RNA world has been introduced for a prebiotic scenario that is dominated by RNA molecules and their properties, in particular their capabilities to act as templates for reproduction and as catalysts for several cleavage and ligation reactions of polynucleotides and polypeptides. This notion is used here also for simple experimental assays which are well suited to study evolution in the test tube. In molecular evolution experiments fitness is determined in essence by the molecular structures of RNA molecules. Evidence is presented for adaptation to environment in cell-free media. RNA based molecular evolution experiments have led to interesting spin-offs in biotechnology, commonly called 'applied molecular evolution', which make use of Darwinian trial-and-error strategies in order to synthesize new pharmacological compounds and other advanced materials on a biological basis. Error-propagation in RNA replication leads to formation of mutant spectra called 'quasispecies'. An increase in the error rate broadens the mutant spectrum. There exists a sharply defined threshold beyond which heredity breaks down and evolutionary adaptation becomes impossible. Almost all RNA viruses studied so far operate at conditions close to this error threshold. Quasispecies and error thresholds are important for an understanding of RNA virus evolution, and they may help to develop novel antiviral strategies. Evolution of RNA molecules can be studied and interpreted by considering secondary structures. The notion of sequence space introduces a distance between pairs of RNA sequences which is tantamount to counting the minimal number of point mutations required to convert the sequences into each other. The mean sensitivity of RNA secondary structures to mutation depends strongly on the base pairing alphabet: structures from sequences which contain only one base pair (GC or AU are much less stable against mutation than those derived from the natural (AUGC) sequences
Sandboxes for Model-Based Inquiry
ERIC Educational Resources Information Center
Brady, Corey; Holbert, Nathan; Soylu, Firat; Novak, Michael; Wilensky, Uri
2015-01-01
In this article, we introduce a class of constructionist learning environments that we call "Emergent Systems Sandboxes" ("ESSs"), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual…
Model-Based Inquiries in Chemistry
ERIC Educational Resources Information Center
Khan, Samia
2007-01-01
In this paper, instructional strategies for sustaining model-based inquiry in an undergraduate chemistry class were analyzed through data collected from classroom observations, a student survey, and in-depth problem-solving sessions with the instructor and students. Analysis of teacher-student interactions revealed a cyclical pattern in which…
Model-Based Diagnostics for Propellant Loading Systems
NASA Technical Reports Server (NTRS)
Daigle, Matthew John; Foygel, Michael; Smelyanskiy, Vadim N.
2011-01-01
The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.
Evolutionary algorithm for the neutrino factory front end design
Poklonskiy, Alexey A.; Neuffer, David; /Fermilab
2009-01-01
The Neutrino Factory is an important tool in the long-term neutrino physics program. Substantial effort is put internationally into designing this facility in order to achieve desired performance within the allotted budget. This accelerator is a secondary beam machine: neutrinos are produced by means of the decay of muons. Muons, in turn, are produced by the decay of pions, produced by hitting the target by a beam of accelerated protons suitable for acceleration. Due to the physics of this process, extra conditioning of the pion beam coming from the target is needed in order to effectively perform subsequent acceleration. The subsystem of the Neutrino Factory that performs this conditioning is called Front End, its main performance characteristic is the number of the produced muons.
PARALLEL MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR WASTE SOLVENT RECYCLING
Waste solvents are of great concern to the chemical process industries and to the public, and many technologies have been suggested and implemented in the chemical process industries to reduce waste and associated environmental impacts. In this article we have developed a novel p...
Evolutionary theory, psychiatry, and psychopharmacology.
Stein, Dan J
2006-07-01
Darwin's seminal publications in the nineteenth century laid the foundation for an evolutionary approach to psychology and psychiatry. Advances in 20th century evolutionary theory facilitated the development of evolutionary psychology and psychiatry as recognized areas of scientific investigation. In this century, advances in understanding the molecular basis of evolution, of the mind, and of psychopathology, offer the possibility of an integrated approach to understanding the proximal (psychobiological) and distal (evolutionary) mechanisms of interest to psychiatry and psychopharmacology. There is, for example, growing interest in the question of whether specific genetic variants mediate psychobiological processes that have evolutionary value in specific contexts, and of the implications of this for understanding the vulnerability to psychopathology and for considering the advantages and limitations of pharmacotherapy. The evolutionary value, and gene-environmental mediation, of early life programming is potentially a particularly rich area of investigation. Although evolutionary approaches to psychology and to medicine face important conceptual and methodological challenges, current work is increasingly sophisticated, and may prove to be an important foundational discipline for clinicians and researchers in psychiatry and psychopharmacology.
A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics
NASA Astrophysics Data System (ADS)
Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.
2015-12-01
This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.
A growth model based on the arithmetic Z-game
NASA Astrophysics Data System (ADS)
Cobeli, Cristian; Prunescu, Mihai; Zaharescu, Alexandru
2016-10-01
We present an evolutionary self-governing model based on the numerical atomic rule $Z(a,b)=ab/\\gcd(a,b)^2$, for $a,b$ positive integers. Starting with a sequence of numbers, the initial generation $Gin$, a new sequence is obtained by applying the $Z$-rule to any neighbor terms. Likewise, applying repeatedly the same procedure to the newest generation, an entire matrix $T_{Gin}$ is generated. Most often, this matrix, which is the recorder of the whole process, shows a fractal aspect and has intriguing properties. If $Gin$ is the sequence of positive integers, in the associated matrix remarkable are the distinguished geometrical figures called the $Z$-solitons and the sinuous evolution of the size of numbers on the western edge. We observe that $T_{\\mathbb{N}^*}$ is close to the analogue free of solitons matrix generated from an initial generation in which each natural number is replaced by its largest divisor that is a product of distinct primes. We describe the shape and the properties of this new matrix. N. J. A. Sloane raised a few interesting problems regarding the western edge of the matrix $T_{\\mathbb{N}^*}$. We solve one of them and present arguments for a precise conjecture on another.
Discovery and Optimization of Materials Using Evolutionary Approaches.
Le, Tu C; Winkler, David A
2016-05-25
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
Discovery and Optimization of Materials Using Evolutionary Approaches.
Le, Tu C; Winkler, David A
2016-05-25
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries. PMID:27171499
Evolutionary adaptive eye tracking for low-cost human computer interaction applications
NASA Astrophysics Data System (ADS)
Shen, Yan; Shin, Hak Chul; Sung, Won Jun; Khim, Sarang; Kim, Honglak; Rhee, Phill Kyu
2013-01-01
We present an evolutionary adaptive eye-tracking framework aiming for low-cost human computer interaction. The main focus is to guarantee eye-tracking performance without using high-cost devices and strongly controlled situations. The performance optimization of eye tracking is formulated into the dynamic control problem of deciding on an eye tracking algorithm structure and associated thresholds/parameters, where the dynamic control space is denoted by genotype and phenotype spaces. The evolutionary algorithm is responsible for exploring the genotype control space, and the reinforcement learning algorithm organizes the evolved genotype into a reactive phenotype. The evolutionary algorithm encodes an eye-tracking scheme as a genetic code based on image variation analysis. Then, the reinforcement learning algorithm defines internal states in a phenotype control space limited by the perceived genetic code and carries out interactive adaptations. The proposed method can achieve optimal performance by compromising the difficulty in the real-time performance of the evolutionary algorithm and the drawback of the huge search space of the reinforcement learning algorithm. Extensive experiments were carried out using webcam image sequences and yielded very encouraging results. The framework can be readily applied to other low-cost vision-based human computer interactions in solving their intrinsic brittleness in unstable operational environments.
A model-based approach to reactive self-configuring systems
Williams, B.C.; Nayak, P.P.
1996-12-31
This paper describes Livingstone, an implemented kernel for a model-based reactive self-configuring autonomous system. It presents a formal characterization of Livingstone`s representation formalism, and reports on our experience with the implementation in a variety of domains. Livingstone provides a reactive system that performs significant deduction in the sense/response loop by drawing on our past experience at building fast propositional conflict-based algorithms for model-based diagnosis, and by framing a model-based configuration manager as a propositional feedback controller that generates focused, optimal responses. Livingstone`s representation formalism achieves broad coverage of hybrid hardware/software systems by coupling the transition system models underlying concurrent reactive languages with the qualitative representations developed in model-based reasoning. Livingstone automates a wide variety of tasks using a single model and a single core algorithm, thus making significant progress towards achieving a central goal of model-based reasoning. Livingstone, together with the HSTS planning and scheduling engine and the RAPS executive, has been selected as part of the core autonomy architecture for NASA`s first New Millennium spacecraft.
Evolutionary Computing Methods for Spectral Retrieval
NASA Technical Reports Server (NTRS)
Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna
2009-01-01
A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.
A review of estimation of distribution algorithms in bioinformatics
Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro
2008-01-01
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112
Configurable pattern-based evolutionary biclustering of gene expression data
2013-01-01
Background Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. Results Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). Conclusions We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology. PMID:23433178
Subwavelength Lattice Optics by Evolutionary Design
2015-01-01
This paper describes a new class of structured optical materials—lattice opto-materials—that can manipulate the flow of visible light into a wide range of three-dimensional profiles using evolutionary design principles. Lattice opto-materials are based on the discretization of a surface into a two-dimensional (2D) subwavelength lattice whose individual lattice sites can be controlled to achieve a programmed optical response. To access a desired optical property, we designed a lattice evolutionary algorithm that includes and optimizes contributions from every element in the lattice. Lattice opto-materials can exhibit simple properties, such as on- and off-axis focusing, and can also concentrate light into multiple, discrete spots. We expanded the unit cell shapes of the lattice to achieve distinct, polarization-dependent optical responses from the same 2D patterned substrate. Finally, these lattice opto-materials can also be combined into architectures that resemble a new type of compound flat lens. PMID:25380062
Evolutionary Optimization of a Geometrically Refined Truss
NASA Technical Reports Server (NTRS)
Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This Technical Publication (TP) presents a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: genetic algorithms and differential evolution to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this TP, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.
Evolutionary psychology: a house built on sand.
Saunders, Peter T
2013-01-01
While Darwinism has contributed much to our understanding of the living world, it has not given us an adequate account of why organisms are the way they are and how they came to be that way. For that we will need all of science, not just a single algorithm. The crucial contribution of Darwinism to biology is that it explains how we can have functional physical traits without a creator. This is less important in psychology because no one is surprised when people behave in ways that work to their advantage. Evolutionary psychology nevertheless follows the Darwinian model. It assumes from the outset that the brain is largely modular and that human nature is made up of a very large number of functionally specialized psychological mechanisms that have been constructed over time by natural selection. How much confidence one should have in its conclusions depends very much on how far one accepts its premises.
Evolutionary complexity for protection of critical assets.
Battaile, Corbett Chandler; Chandross, Michael Evan
2005-01-01
This report summarizes the work performed as part of a one-year LDRD project, 'Evolutionary Complexity for Protection of Critical Assets.' A brief introduction is given to the topics of genetic algorithms and genetic programming, followed by a discussion of relevant results obtained during the project's research, and finally the conclusions drawn from those results. The focus is on using genetic programming to evolve solutions for relatively simple algebraic equations as a prototype application for evolving complexity in computer codes. The results were obtained using the lil-gp genetic program, a C code for evolving solutions to user-defined problems and functions. These results suggest that genetic programs are not well-suited to evolving complexity for critical asset protection because they cannot efficiently evolve solutions to complex problems, and introduce unacceptable performance penalties into solutions for simple ones.
Evolutionary optimization of rotational population transfer
Rouzee, Arnaud; Vrakking, Marc J. J.; Ghafur, Omair; Gijsbertsen, Arjan; Vidma, Konstantin; Meijer, Afric; Zande, Wim J. van der; Parker, David; Shir, Ofer M.; Baeck, Thomas
2011-09-15
We present experimental and numerical studies on control of rotational population transfer of NO(J=1/2) molecules to higher rotational states. We are able to transfer 57% of the population to the J=5/2 state and 46% to J=9/2, in good agreement with quantum mechanical simulations. The optimal pulse shapes are composed of pulse sequences with delays corresponding to the beat frequencies of states on the rotational ladder. The evolutionary algorithm is limited by experimental constraints such as volume averaging and the finite laser intensity used, the latter to circumvent ionization. Without these constraints, near-perfect control (>98%) is possible. In addition, we show that downward control, moving molecules from high to low rotational states, is also possible.
Army ants: an evolutionary bestseller?
Berghoff, Stefanie M
2003-09-01
Army ants are characterized by a complex combination of behavioral and morphological traits. Molecular data now indicate that army ant behavior has a unique evolutionary origin and has been conserved for over more than 100 million years.
Evolutionary constraints or opportunities?
Sharov, Alexei A.
2014-01-01
Natural selection is traditionally viewed as a leading factor of evolution, whereas variation is assumed to be random and non-directional. Any order in variation is attributed to epigenetic or developmental constraints that can hinder the action of natural selection. In contrast I consider the positive role of epigenetic mechanisms in evolution because they provide organisms with opportunities for rapid adaptive change. Because the term “constraint” has negative connotations, I use the term “regulated variation” to emphasize the adaptive nature of phenotypic variation, which helps populations and species to survive and evolve in changing environments. The capacity to produce regulated variation is a phenotypic property, which is not described in the genome. Instead, the genome acts as a switchboard, where mostly random mutations switch “on” or “off” preexisting functional capacities of organism components. Thus, there are two channels of heredity: informational (genomic) and structure-functional (phenotypic). Functional capacities of organisms most likely emerged in a chain of modifications and combinations of more simple ancestral functions. The role of DNA has been to keep records of these changes (without describing the result) so that they can be reproduced in the following generations. Evolutionary opportunities include adjustments of individual functions, multitasking, connection between various components of an organism, and interaction between organisms. The adaptive nature of regulated variation can be explained by the differential success of lineages in macro-evolution. Lineages with more advantageous patterns of regulated variation are likely to produce more species and secure more resources (i.e., long-term lineage selection). PMID:24769155
Evolutionary constraints or opportunities?
Sharov, Alexei A
2014-04-22
Natural selection is traditionally viewed as a leading factor of evolution, whereas variation is assumed to be random and non-directional. Any order in variation is attributed to epigenetic or developmental constraints that can hinder the action of natural selection. In contrast I consider the positive role of epigenetic mechanisms in evolution because they provide organisms with opportunities for rapid adaptive change. Because the term "constraint" has negative connotations, I use the term "regulated variation" to emphasize the adaptive nature of phenotypic variation, which helps populations and species to survive and evolve in changing environments. The capacity to produce regulated variation is a phenotypic property, which is not described in the genome. Instead, the genome acts as a switchboard, where mostly random mutations switch "on" or "off" preexisting functional capacities of organism components. Thus, there are two channels of heredity: informational (genomic) and structure-functional (phenotypic). Functional capacities of organisms most likely emerged in a chain of modifications and combinations of more simple ancestral functions. The role of DNA has been to keep records of these changes (without describing the result) so that they can be reproduced in the following generations. Evolutionary opportunities include adjustments of individual functions, multitasking, connection between various components of an organism, and interaction between organisms. The adaptive nature of regulated variation can be explained by the differential success of lineages in macro-evolution. Lineages with more advantageous patterns of regulated variation are likely to produce more species and secure more resources (i.e., long-term lineage selection).
Evolutionary constraints or opportunities?
Sharov, Alexei A
2014-09-01
Natural selection is traditionally viewed as a leading factor of evolution, whereas variation is assumed to be random and non-directional. Any order in variation is attributed to epigenetic or developmental constraints that can hinder the action of natural selection. In contrast I consider the positive role of epigenetic mechanisms in evolution because they provide organisms with opportunities for rapid adaptive change. Because the term "constraint" has negative connotations, I use the term "regulated variation" to emphasize the adaptive nature of phenotypic variation, which helps populations and species to survive and evolve in changing environments. The capacity to produce regulated variation is a phenotypic property, which is not described in the genome. Instead, the genome acts as a switchboard, where mostly random mutations switch "on" or "off" preexisting functional capacities of organism components. Thus, there are two channels of heredity: informational (genomic) and structure-functional (phenotypic). Functional capacities of organisms most likely emerged in a chain of modifications and combinations of more simple ancestral functions. The role of DNA has been to keep records of these changes (without describing the result) so that they can be reproduced in the following generations. Evolutionary opportunities include adjustments of individual functions, multitasking, connection between various components of an organism, and interaction between organisms. The adaptive nature of regulated variation can be explained by the differential success of lineages in macro-evolution. Lineages with more advantageous patterns of regulated variation are likely to produce more species and secure more resources (i.e., long-term lineage selection).
Molluscan Evolutionary Genomics
Simison, W. Brian; Boore, Jeffrey L.
2005-12-01
In the last 20 years there have been dramatic advances in techniques of high-throughput DNA sequencing, most recently accelerated by the Human Genome Project, a program that has determined the three billion base pair code on which we are based. Now this tremendous capability is being directed at other genome targets that are being sampled across the broad range of life. This opens up opportunities as never before for evolutionary and organismal biologists to address questions of both processes and patterns of organismal change. We stand at the dawn of a new 'modern synthesis' period, paralleling that of the early 20th century when the fledgling field of genetics first identified the underlying basis for Darwin's theory. We must now unite the efforts of systematists, paleontologists, mathematicians, computer programmers, molecular biologists, developmental biologists, and others in the pursuit of discovering what genomics can teach us about the diversity of life. Genome-level sampling for mollusks to date has mostly been limited to mitochondrial genomes and it is likely that these will continue to provide the best targets for broad phylogenetic sampling in the near future. However, we are just beginning to see an inroad into complete nuclear genome sequencing, with several mollusks and other eutrochozoans having been selected for work about to begin. Here, we provide an overview of the state of molluscan mitochondrial genomics, highlight a few of the discoveries from this research, outline the promise of broadening this dataset, describe upcoming projects to sequence whole mollusk nuclear genomes, and challenge the community to prepare for making the best use of these data.
Facial Composite System Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zahradníková, Barbora; Duchovičová, Soňa; Schreiber, Peter
2014-12-01
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
Statistical shape model-based femur kinematics from biplane fluoroscopy.
Baka, N; de Bruijne, M; van Walsum, T; Kaptein, B L; Giphart, J E; Schaap, M; Niessen, W J; Lelieveldt, B P F
2012-08-01
Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).
Automatic sensor placement for model-based robot vision.
Chen, S Y; Li, Y F
2004-02-01
This paper presents a method for automatic sensor placement for model-based robot vision. In such a vision system, the sensor often needs to be moved from one pose to another around the object to observe all features of interest. This allows multiple three-dimensional (3-D) images to be taken from different vantage viewpoints. The task involves determination of the optimal sensor placements and a shortest path through these viewpoints. During the sensor planning, object features are resampled as individual points attached with surface normals. The optimal sensor placement graph is achieved by a genetic algorithm in which a min-max criterion is used for the evaluation. A shortest path is determined by Christofides algorithm. A Viewpoint Planner is developed to generate the sensor placement plan. It includes many functions, such as 3-D animation of the object geometry, sensor specification, initialization of the viewpoint number and their distribution, viewpoint evolution, shortest path computation, scene simulation of a specific viewpoint, parameter amendment. Experiments are also carried out on a real robot vision system to demonstrate the effectiveness of the proposed method.
Model based iterative reconstruction for Bright Field electron tomography
NASA Astrophysics Data System (ADS)
Venkatakrishnan, Singanallur V.; Drummy, Lawrence F.; De Graef, Marc; Simmons, Jeff P.; Bouman, Charles A.
2013-02-01
Bright Field (BF) electron tomography (ET) has been widely used in the life sciences to characterize biological specimens in 3D. While BF-ET is the dominant modality in the life sciences it has been generally avoided in the physical sciences due to anomalous measurements in the data due to a phenomenon called "Bragg scatter" - visible when crystalline samples are imaged. These measurements cause undesirable artifacts in the reconstruction when the typical algorithms such as Filtered Back Projection (FBP) and Simultaneous Iterative Reconstruction Technique (SIRT) are applied to the data. Model based iterative reconstruction (MBIR) provides a powerful framework for tomographic reconstruction that incorporates a model for data acquisition, noise in the measurement and a model for the object to obtain reconstructions that are qualitatively superior and quantitatively accurate. In this paper we present a novel MBIR algorithm for BF-ET which accounts for the presence of anomalous measurements from Bragg scatter in the data during the iterative reconstruction. Our method accounts for the anomalies by formulating the reconstruction as minimizing a cost function which rejects measurements that deviate significantly from the typical Beer's law model widely assumed for BF-ET. Results on simulated as well as real data show that our method can dramatically improve the reconstructions compared to FBP and MBIR without anomaly rejection, suppressing the artifacts due to the Bragg anomalies.
Model-based Processing of Micro-cantilever Sensor Arrays
Tringe, J W; Clague, D S; Candy, J V; Lee, C L; Rudd, R E; Burnham, A K
2004-11-17
We develop a model-based processor (MBP) for a micro-cantilever array sensor to detect target species in solution. After discussing the generalized framework for this problem, we develop the specific model used in this study. We perform a proof-of-concept experiment, fit the model parameters to the measured data and use them to develop a Gauss-Markov simulation. We then investigate two cases of interest: (1) averaged deflection data, and (2) multi-channel data. In both cases the evaluation proceeds by first performing a model-based parameter estimation to extract the model parameters, next performing a Gauss-Markov simulation, designing the optimal MBP and finally applying it to measured experimental data. The simulation is used to evaluate the performance of the MBP in the multi-channel case and compare it to a ''smoother'' (''averager'') typically used in this application. It was shown that the MBP not only provides a significant gain ({approx} 80dB) in signal-to-noise ratio (SNR), but also consistently outperforms the smoother by 40-60 dB. Finally, we apply the processor to the smoothed experimental data and demonstrate its capability for chemical detection. The MBP performs quite well, though it includes a correctable systematic bias error. The project's primary accomplishment was the successful application of model-based processing to signals from micro-cantilever arrays: 40-60 dB improvement vs. the smoother algorithm was demonstrated. This result was achieved through the development of appropriate mathematical descriptions for the chemical and mechanical phenomena, and incorporation of these descriptions directly into the model-based signal processor. A significant challenge was the development of the framework which would maximize the usefulness of the signal processing algorithms while ensuring the accuracy of the mathematical description of the chemical-mechanical signal. Experimentally, the difficulty was to identify and characterize the non
Evolutionary status of Be stars
NASA Astrophysics Data System (ADS)
Zorec, J.; Frémat, Y.; Cidale, L.
2004-12-01
Fundamental parameters of nearly 50 field Be stars have been determined. Correcting these parameters from gravity darkening effects induced the fast rotation, we deduced the evolutionary phase of the studied stars. We show that the evolutionary phase at which appear the Be phenomenon is mass dependent: the smaller the stellar mass the elder the phase in the main sequence at which the Be phenomenon seem to appear.
Model Based Testing for Agent Systems
NASA Astrophysics Data System (ADS)
Zhang, Zhiyong; Thangarajah, John; Padgham, Lin
Although agent technology is gaining world wide popularity, a hindrance to its uptake is the lack of proper testing mechanisms for agent based systems. While many traditional software testing methods can be generalized to agent systems, there are many aspects that are different and which require an understanding of the underlying agent paradigm. In this paper we present certain aspects of a testing framework that we have developed for agent based systems. The testing framework is a model based approach using the design models of the Prometheus agent development methodology. In this paper we focus on model based unit testing and identify the appropriate units, present mechanisms for generating suitable test cases and for determining the order in which the units are to be tested, present a brief overview of the unit testing process and an example. Although we use the design artefacts from Prometheus the approach is suitable for any plan and event based agent system.
Systems Engineering Interfaces: A Model Based Approach
NASA Technical Reports Server (NTRS)
Fosse, Elyse; Delp, Christopher
2013-01-01
Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.
Chakraborty, Chiranjib; Agoramoorthy, Govindasamy; Hsu, Minna J.
2011-01-01
Insulin receptor substrate (IRS) harbors proteins such as IRS1, IRS2, IRS3, IRS4, IRS5 and IRS6. These key proteins act as vital downstream regulators in the insulin signaling pathway. However, little is known about the evolutionary relationship among the IRS family members. This study explores the potential to depict the evolutionary relationship among the IRS family using bioinformatics, algorithm analysis and mathematical models. PMID:21364910
Evolutionary conceptual analysis: faith community nursing.
Ziebarth, Deborah
2014-12-01
knowledge and model-based applications for evidence-based practice and research. PMID:25097106
The origins of counting algorithms.
Cantlon, Jessica F; Piantadosi, Steven T; Ferrigno, Stephen; Hughes, Kelly D; Barnard, Allison M
2015-06-01
Humans' ability to count by verbally labeling discrete quantities is unique in animal cognition. The evolutionary origins of counting algorithms are not understood. We report that nonhuman primates exhibit a cognitive ability that is algorithmically and logically similar to human counting. Monkeys were given the task of choosing between two food caches. First, they saw one cache baited with some number of food items, one item at a time. Then, a second cache was baited with food items, one at a time. At the point when the second set was approximately equal to the first set, the monkeys spontaneously moved to choose the second set even before that cache was completely baited. Using a novel Bayesian analysis, we show that the monkeys used an approximate counting algorithm for comparing quantities in sequence that is incremental, iterative, and condition controlled. This proto-counting algorithm is structurally similar to formal counting in humans and thus may have been an important evolutionary precursor to human counting. PMID:25953949
How cultural evolutionary theory can inform social psychology and vice versa.
Mesoudi, Alex
2009-10-01
Cultural evolutionary theory is an interdisciplinary field in which human culture is viewed as a Darwinian process of variation, competition, and inheritance, and the tools, methods, and theories developed by evolutionary biologists to study genetic evolution are adapted to study cultural change. It is argued here that an integration of the theories and findings of mainstream social psychology and of cultural evolutionary theory can be mutually beneficial. Social psychology provides cultural evolution with a set of empirically verified microevolutionary cultural processes, such as conformity, model-based biases, and content biases, that are responsible for specific patterns of cultural change. Cultural evolutionary theory provides social psychology with ultimate explanations for, and an understanding of the population-level consequences of, many social psychological phenomena, such as social learning, conformity, social comparison, and intergroup processes, as well as linking social psychology with other social science disciplines such as cultural anthropology, archaeology, and sociology.
Model-based vision for car following
NASA Astrophysics Data System (ADS)
Schneiderman, Henry; Nashman, Marilyn; Lumia, Ronald
1993-08-01
This paper describes a vision processing algorithm that supports autonomous car following. The algorithm visually tracks the position of a `lead vehicle' from the vantage of a pursuing `chase vehicle.' The algorithm requires a 2-D model of the back of the lead vehicle. This model is composed of line segments corresponding to features that give rise to strong edges. There are seven sequential stages of computation: (1) Extracting edge points; (2) Associating extracted edge points with the model features; (3) Determining the position of each model feature; (4) Determining the model position; (5) Updating the motion model of the object; (6) Predicting the position of the object in next image; (7) Predicting the location of all object features from prediction of object position. All processing is confined to the 2-D image plane. The 2-D model location computed in this processing is used to determine the position of the lead vehicle with respect to a 3-D coordinate frame affixed to the chase vehicle. This algorithm has been used as part of a complete system to drive an autonomous vehicle, a High Mobility Multipurpose Wheeled Vehicle (HMMWV) such that it follows a lead vehicle at speeds up to 35 km/hr. The algorithm runs at an update rate of 15 Hertz and has a worst case computational delay of 128 ms. The algorithm is implemented under the NASA/NBS Standard Reference Model for Telerobotic Control System Architecture (NASREM) and runs on a dedicated vision processing engine and a VME-based multiprocessor system.
A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search
Hart, W.E.
1999-04-22
The authors present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R{sup n}: evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. They show that EPSAs can be cast as stochastic pattern search methods, and they use this observation to prove that EpSAs have a probabilistic weak stationary point convergence theory. This work provides the first convergence analysis for a class of evolutionary algorithms that guarantees convergence almost surely to a stationary point of a nonconvex objective function.
Inferring the evolutionary history of gene clusters from phylogenetic and gene order data.
Lajoie, Mathieu; Bertrand, Denis; El-Mabrouk, Nadia
2010-04-01
Gene duplication is frequent within gene clusters and plays a fundamental role in evolution by providing a source of new genetic material upon which natural selection can act. Although classical phylogenetic inference methods provide some insight into the evolutionary history of a gene cluster, they are not sufficient alone to differentiate single- from multiple gene duplication events and to answer other questions regarding the nature and size of evolutionary events. In this paper, we present an algorithm allowing to infer a set of optimal evolutionary histories for a gene cluster in a single species, according to a general cost model involving variable length duplications (in tandem or inverted), deletions, and inversions. We applied our algorithm to the human olfactory receptor and protocadherin gene clusters, showing that the duplication size distribution differs significantly between the two gene families. The algorithm is available through a web interface at http://www-lbit.iro.umontreal.ca/DILTAG/.
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.
Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology.
Delport, Wayne; Poon, Art F Y; Frost, Simon D W; Kosakovsky Pond, Sergei L
2010-10-01
Datamonkey is a popular web-based suite of phylogenetic analysis tools for use in evolutionary biology. Since the original release in 2005, we have expanded the analysis options to include recently developed algorithmic methods for recombination detection, evolutionary fingerprinting of genes, codon model selection, co-evolution between sites, identification of sites, which rapidly escape host-immune pressure and HIV-1 subtype assignment. The traditional selection tools have also been augmented to include recent developments in the field. Here, we summarize the analyses options currently available on Datamonkey, and provide guidelines for their use in evolutionary biology. Availability and documentation: http://www.datamonkey.org.
Comparison of the Asynchronous Differential Evolution and JADE Minimization Algorithms
NASA Astrophysics Data System (ADS)
Zhabitsky, Mikhail
2016-02-01
Differential Evolution (DE) is an efficient evolutionary algorithm to solve global optimization problems. In this work we compare performance of the recently proposed Asynchronous Differential Evolution with Adaptive Correlation Matrix (ADEACM) to the widely used JADE algorithm, a DE variant with adaptive control parameters.
Preparation Model Based Control System For Hot Steel Strip Rolling Mill Stands
NASA Astrophysics Data System (ADS)
Bouazza, S. E.; Abbassi, H. A.; Moussaoui, A. K.
2008-06-01
As part of a research project on El-hadjar Hot Steel Rolling Mill Plant Annaba Algeria a new Model based control system is suggested to improve the performance of the hot strip rolling mill process. In this paper off-line model based controllers and a process simulator are described. The process models are based on the laws of physics. these models can predict the future behavior and the stability of the controlled process very reliably. The control scheme consists of a control algorithm. This Model based Control system is evaluated on a simulation model that represents accurately the dynamic of the process. Finally the usefulness to the Steel Industry of the suggested method is highlighted.
Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization.
Yu, Zhou; Thibault, Jean-Baptiste; Bouman, Charles A; Sauer, Ken D; Hsieh, Jiang
2011-01-01
Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.
Improved initialisation of model-based clustering using Gaussian hierarchical partitions
Scrucca, Luca; Raftery, Adrian E.
2015-01-01
Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. This choice is computationally convenient and often yields good clustering partitions. However, in certain circumstances, poor initial partitions may cause the EM algorithm to converge to a local maximum of the likelihood function. We propose several simple and fast refinements based on data transformations and illustrate them through data examples. PMID:26949421
Evolutionary inevitability of sexual antagonism.
Connallon, Tim; Clark, Andrew G
2014-02-01
Sexual antagonism, whereby mutations are favourable in one sex and disfavourable in the other, is common in natural populations, yet the root causes of sexual antagonism are rarely considered in evolutionary theories of adaptation. Here, we explore the evolutionary consequences of sex-differential selection and genotype-by-sex interactions for adaptation in species with separate sexes. We show that sexual antagonism emerges naturally from sex differences in the direction of selection on phenotypes expressed by both sexes or from sex-by-genotype interactions affecting the expression of such phenotypes. Moreover, modest sex differences in selection or genotype-by-sex effects profoundly influence the long-term evolutionary trajectories of populations with separate sexes, as these conditions trigger the evolution of strong sexual antagonism as a by-product of adaptively driven evolutionary change. The theory demonstrates that sexual antagonism is an inescapable by-product of adaptation in species with separate sexes, whether or not selection favours evolutionary divergence between males and females.
Evolutionary genetics of maternal effects
Wolf, Jason B.; Wade, Michael J.
2016-01-01
Maternal genetic effects (MGEs), where genes expressed by mothers affect the phenotype of their offspring, are important sources of phenotypic diversity in a myriad of organisms. We use a single‐locus model to examine how MGEs contribute patterns of heritable and nonheritable variation and influence evolutionary dynamics in randomly mating and inbreeding populations. We elucidate the influence of MGEs by examining the offspring genotype‐phenotype relationship, which determines how MGEs affect evolutionary dynamics in response to selection on offspring phenotypes. This approach reveals important results that are not apparent from classic quantitative genetic treatments of MGEs. We show that additive and dominance MGEs make different contributions to evolutionary dynamics and patterns of variation, which are differentially affected by inbreeding. Dominance MGEs make the offspring genotype‐phenotype relationship frequency dependent, resulting in the appearance of negative frequency‐dependent selection, while additive MGEs contribute a component of parent‐of‐origin dependent variation. Inbreeding amplifies the contribution of MGEs to the additive genetic variance and, therefore enhances their evolutionary response. Considering evolutionary dynamics of allele frequency change on an adaptive landscape, we show that this landscape differs from the mean fitness surface, and therefore, under some condition, fitness peaks can exist but not be “available” to the evolving population. PMID:26969266
The major synthetic evolutionary transitions.
Solé, Ricard
2016-08-19
Evolution is marked by well-defined events involving profound innovations that are known as 'major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These 'synthetic' transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined.This article is part of the themed issue 'The major synthetic evolutionary transitions'. PMID:27431528
The major synthetic evolutionary transitions.
Solé, Ricard
2016-08-19
Evolution is marked by well-defined events involving profound innovations that are known as 'major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These 'synthetic' transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined.This article is part of the themed issue 'The major synthetic evolutionary transitions'.
Environmental changes bridge evolutionary valleys
Steinberg, Barrett; Ostermeier, Marc
2016-01-01
In the basic fitness landscape metaphor for molecular evolution, evolutionary pathways are presumed to follow uphill steps of increasing fitness. How evolution can cross fitness valleys is an open question. One possibility is that environmental changes alter the fitness landscape such that low-fitness sequences reside on a hill in alternate environments. We experimentally test this hypothesis on the antibiotic resistance gene TEM-15 β-lactamase by comparing four evolutionary strategies shaped by environmental changes. The strategy that included initial steps of selecting for low antibiotic resistance (negative selection) produced superior alleles compared with the other three strategies. We comprehensively examined possible evolutionary pathways leading to one such high-fitness allele and found that an initially deleterious mutation is key to the allele’s evolutionary history. This mutation is an initial gateway to an otherwise relatively inaccessible area of sequence space and participates in higher-order, positive epistasis with a number of neutral to slightly beneficial mutations. The ability of negative selection and environmental changes to provide access to novel fitness peaks has important implications for natural evolutionary mechanisms and applied directed evolution. PMID:26844293
Evolutionary foundations for cancer biology
Aktipis, C Athena; Nesse, Randolph M
2013-01-01
New applications of evolutionary biology are transforming our understanding of cancer. The articles in this special issue provide many specific examples, such as microorganisms inducing cancers, the significance of within-tumor heterogeneity, and the possibility that lower dose chemotherapy may sometimes promote longer survival. Underlying these specific advances is a large-scale transformation, as cancer research incorporates evolutionary methods into its toolkit, and asks new evolutionary questions about why we are vulnerable to cancer. Evolution explains why cancer exists at all, how neoplasms grow, why cancer is remarkably rare, and why it occurs despite powerful cancer suppression mechanisms. Cancer exists because of somatic selection; mutations in somatic cells result in some dividing faster than others, in some cases generating neoplasms. Neoplasms grow, or do not, in complex cellular ecosystems. Cancer is relatively rare because of natural selection; our genomes were derived disproportionally from individuals with effective mechanisms for suppressing cancer. Cancer occurs nonetheless for the same six evolutionary reasons that explain why we remain vulnerable to other diseases. These four principles—cancers evolve by somatic selection, neoplasms grow in complex ecosystems, natural selection has shaped powerful cancer defenses, and the limitations of those defenses have evolutionary explanations—provide a foundation for understanding, preventing, and treating cancer. PMID:23396885
The major synthetic evolutionary transitions
Solé, Ricard
2016-01-01
Evolution is marked by well-defined events involving profound innovations that are known as ‘major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These ‘synthetic’ transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined. This article is part of the themed issue ‘The major synthetic evolutionary transitions’. PMID:27431528
Structural symmetry in evolutionary games
McAvoy, Alex; Hauert, Christoph
2015-01-01
In evolutionary game theory, an important measure of a mutant trait (strategy) is its ability to invade and take over an otherwise-monomorphic population. Typically, one quantifies the success of a mutant strategy via the probability that a randomly occurring mutant will fixate in the population. However, in a structured population, this fixation probability may depend on where the mutant arises. Moreover, the fixation probability is just one quantity by which one can measure the success of a mutant; fixation time, for instance, is another. We define a notion of homogeneity for evolutionary games that captures what it means for two single-mutant states, i.e. two configurations of a single mutant in an otherwise-monomorphic population, to be ‘evolutionarily equivalent’ in the sense that all measures of evolutionary success are the same for both configurations. Using asymmetric games, we argue that the term ‘homogeneous’ should apply to the evolutionary process as a whole rather than to just the population structure. For evolutionary matrix games in graph-structured populations, we give precise conditions under which the resulting process is homogeneous. Finally, we show that asymmetric matrix games can be reduced to symmetric games if the population structure possesses a sufficient degree of symmetry. PMID:26423436
Evolutionary genomics of environmental pollution.
Whitehead, Andrew
2014-01-01
Chemical toxins have been a persistent source of evolutionary challenges throughout the history of life, and deep within the genomic storehouse of evolutionary history lay ancient adaptations to diverse chemical poisons. However, the rate of change of contemporary environments mediated by human-introduced pollutants is rapidly screening this storehouse and severely testing the adaptive potential of many species. In this chapter, we briefly review the deep history of evolutionary adaptation to environmental toxins, and then proceed to describe the attributes of stressors and populations that may facilitate contemporary adaptation to pollutants introduced by humans. We highlight that phenotypes derived to enable persistence in polluted habitats may be multi-dimensional, requiring global genome-scale tools and approaches to uncover their mechanistic basis, and include examples of recent progress in the field. The modern tools of genomics offer promise for discovering how pollutants interact with genomes on physiological timescales, and also for discovering what genomic attributes of populations may enable resistance to pollutants over evolutionary timescales. Through integration of these sophisticated genomics tools and approaches with an understanding of the deep historical forces that shaped current populations, a more mature understanding of the mechanistic basis of contemporary ecological-evolutionary dynamics should emerge.
Evolutionary genetics of maternal effects.
Wolf, Jason B; Wade, Michael J
2016-04-01
Maternal genetic effects (MGEs), where genes expressed by mothers affect the phenotype of their offspring, are important sources of phenotypic diversity in a myriad of organisms. We use a single-locus model to examine how MGEs contribute patterns of heritable and nonheritable variation and influence evolutionary dynamics in randomly mating and inbreeding populations. We elucidate the influence of MGEs by examining the offspring genotype-phenotype relationship, which determines how MGEs affect evolutionary dynamics in response to selection on offspring phenotypes. This approach reveals important results that are not apparent from classic quantitative genetic treatments of MGEs. We show that additive and dominance MGEs make different contributions to evolutionary dynamics and patterns of variation, which are differentially affected by inbreeding. Dominance MGEs make the offspring genotype-phenotype relationship frequency dependent, resulting in the appearance of negative frequency-dependent selection, while additive MGEs contribute a component of parent-of-origin dependent variation. Inbreeding amplifies the contribution of MGEs to the additive genetic variance and, therefore enhances their evolutionary response. Considering evolutionary dynamics of allele frequency change on an adaptive landscape, we show that this landscape differs from the mean fitness surface, and therefore, under some condition, fitness peaks can exist but not be "available" to the evolving population. PMID:26969266
Model-based neuroimaging for cognitive computing.
Poznanski, Roman R
2009-09-01
The continuity of the mind is suggested to mean the continuous spatiotemporal dynamics arising from the electrochemical signature of the neocortex: (i) globally through volume transmission in the gray matter as fields of neural activity, and (ii) locally through extrasynaptic signaling between fine distal dendrites of cortical neurons. If the continuity of dynamical systems across spatiotemporal scales defines a stream of consciousness then intentional metarepresentations as templates of dynamic continuity allow qualia to be semantically mapped during neuroimaging of specific cognitive tasks. When interfaced with a computer, such model-based neuroimaging requiring new mathematics of the brain will begin to decipher higher cognitive operations not possible with existing brain-machine interfaces.
Model-based Tomographic Reconstruction Literature Search
Chambers, D H; Lehman, S K
2005-11-30
In the process of preparing a proposal for internal research funding, a literature search was conducted on the subject of model-based tomographic reconstruction (MBTR). The purpose of the search was to ensure that the proposed research would not replicate any previous work. We found that the overwhelming majority of work on MBTR which used parameterized models of the object was theoretical in nature. Only three researchers had applied the technique to actual data. In this note, we summarize the findings of the literature search.
A comparison of model-based and hyperbolic localization techniques as applied to marine mammal calls
NASA Astrophysics Data System (ADS)
Tiemann, Christopher O.; Porter, Michael B.
2003-10-01
A common technique for the passive acoustic localization of singing marine mammals is that of hyperbolic fixing. This technique assumes straight-line, constant wave speed acoustic propagation to associate travel time with range, but in some geometries, these assumptions can lead to localization errors. A new localization algorithm based on acoustic propagation models can account for waveguide and multipath effects, and it has successfully been tested against real acoustic data from three different environments (Hawaii, California, and Bahamas) and three different species (humpback, blue, and sperm whales). Accuracy of the model-based approach has been difficult to verify given the absence of concurrent visual and acoustic observations of the same animal. However, the model-based algorithm was recently exercised against a controlled source of known position broadcasting recorded whale sounds, and location estimates were then compared to hyperbolic techniques and true source position. In geometries where direct acoustic paths exist, both model-based and hyperbolic techniques perform equally well. However, in geometries where bathymetric and refractive effects are important, such as at long range, the model-based approach shows improved accuracy.
Model-Based GN and C Simulation and Flight Software Development for Orion Missions beyond LEO
NASA Technical Reports Server (NTRS)
Odegard, Ryan; Milenkovic, Zoran; Henry, Joel; Buttacoli, Michael
2014-01-01
For Orion missions beyond low Earth orbit (LEO), the Guidance, Navigation, and Control (GN&C) system is being developed using a model-based approach for simulation and flight software. Lessons learned from the development of GN&C algorithms and flight software for the Orion Exploration Flight Test One (EFT-1) vehicle have been applied to the development of further capabilities for Orion GN&C beyond EFT-1. Continuing the use of a Model-Based Development (MBD) approach with the Matlab®/Simulink® tool suite, the process for GN&C development and analysis has been largely improved. Furthermore, a model-based simulation environment in Simulink, rather than an external C-based simulation, greatly eases the process for development of flight algorithms. The benefits seen by employing lessons learned from EFT-1 are described, as well as the approach for implementing additional MBD techniques. Also detailed are the key enablers for improvements to the MBD process, including enhanced configuration management techniques for model-based software systems, automated code and artifact generation, and automated testing and integration.
Linear antenna array optimization using flower pollination algorithm.
Saxena, Prerna; Kothari, Ashwin
2016-01-01
Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance. PMID:27066339
An evolutionary approach toward dynamic self-generated fuzzy inference systems.
Zhou, Yi; Er, Meng Joo
2008-08-01
An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods.
Evolutionary psychology and intelligence research.
Kanazawa, Satoshi
2010-01-01
This article seeks to unify two subfields of psychology that have hitherto stood separately: evolutionary psychology and intelligence research/differential psychology. I suggest that general intelligence may simultaneously be an evolved adaptation and an individual-difference variable. Tooby and Cosmides's (1990a) notion of random quantitative variation on a monomorphic design allows us to incorporate heritable individual differences in evolved adaptations. The Savanna-IQ Interaction Hypothesis, which is one consequence of the integration of evolutionary psychology and intelligence research, can potentially explain why less intelligent individuals enjoy TV more, why liberals are more intelligent than conservatives, and why night owls are more intelligent than morning larks, among many other findings. The general approach proposed here will allow us to integrate evolutionary psychology with any other aspect of differential psychology.
Neuronal boost to evolutionary dynamics
de Vladar, Harold P.; Szathmáry, Eörs
2015-01-01
Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild. PMID:26640653
Neuronal boost to evolutionary dynamics.
de Vladar, Harold P; Szathmáry, Eörs
2015-12-01
Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.
Evolutionary models of interstellar chemistry
NASA Technical Reports Server (NTRS)
Prasad, Sheo S.
1987-01-01
The goal of evolutionary models of interstellar chemistry is to understand how interstellar clouds came to be the way they are, how they will change with time, and to place them in an evolutionary sequence with other celestial objects such as stars. An improved Mark II version of an earlier model of chemistry in dynamically evolving clouds is presented. The Mark II model suggests that the conventional elemental C/O ratio less than one can explain the observed abundances of CI and the nondetection of O2 in dense clouds. Coupled chemical-dynamical models seem to have the potential to generate many observable discriminators of the evolutionary tracks. This is exciting, because, in general, purely dynamical models do not yield enough verifiable discriminators of the predicted tracks.
Sandboxes for Model-Based Inquiry
NASA Astrophysics Data System (ADS)
Brady, Corey; Holbert, Nathan; Soylu, Firat; Novak, Michael; Wilensky, Uri
2015-04-01
In this article, we introduce a class of constructionist learning environments that we call Emergent Systems Sandboxes ( ESSs), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual construction environment that support students in creating, exploring, and sharing computational models of dynamic systems that exhibit emergent phenomena. They provide learners with "entity"-level construction primitives that reflect an underlying scientific model. These primitives can be directly "painted" into a sandbox space, where they can then be combined, arranged, and manipulated to construct complex systems and explore the emergent properties of those systems. We argue that ESSs offer a means of addressing some of the key barriers to adopting rich, constructionist model-based inquiry approaches in science classrooms at scale. Situating the ESS in a large-scale science modeling curriculum we are implementing across the USA, we describe how the unique "entity-level" primitive design of an ESS facilitates knowledge system refinement at both an individual and social level, we describe how it supports flexible modeling practices by providing both continuous and discrete modes of executability, and we illustrate how it offers students a variety of opportunities for validating their qualitative understandings of emergent systems as they develop.
The evolutionary psychology of violence.
Goetz, Aaron T
2010-02-01
This paper reviews theory and research on the evolutionary psychology of violence. First, I examine evidence suggesting that humans have experienced an evolutionary history of violence. Next, I discuss violence as a context-sensitive strategy that might have provided benefits to our ancestors under certain circumstances. I then focus on the two most common forms of violence that plague humans -violence over status contests and intimate partner violence- outlining psychological mechanisms involved in each. Finally, I suggest that greater progress will be made by shifting the study from contexts to mechanisms.
The evolutionary psychology of hunger.
Al-Shawaf, Laith
2016-10-01
An evolutionary psychological perspective suggests that emotions can be understood as coordinating mechanisms whose job is to regulate various psychological and physiological programs in the service of solving an adaptive problem. This paper suggests that it may also be fruitful to approach hunger from this coordinating mechanism perspective. To this end, I put forward an evolutionary task analysis of hunger, generating novel a priori hypotheses about the coordinating effects of hunger on psychological processes such as perception, attention, categorization, and memory. This approach appears empirically fruitful in that it yields a bounty of testable new hypotheses.
The evolutionary psychology of hunger.
Al-Shawaf, Laith
2016-10-01
An evolutionary psychological perspective suggests that emotions can be understood as coordinating mechanisms whose job is to regulate various psychological and physiological programs in the service of solving an adaptive problem. This paper suggests that it may also be fruitful to approach hunger from this coordinating mechanism perspective. To this end, I put forward an evolutionary task analysis of hunger, generating novel a priori hypotheses about the coordinating effects of hunger on psychological processes such as perception, attention, categorization, and memory. This approach appears empirically fruitful in that it yields a bounty of testable new hypotheses. PMID:27328100
Deep evolutionary origins of neurobiology
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
Unifying Model-Based and Reactive Programming within a Model-Based Executive
NASA Technical Reports Server (NTRS)
Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)
1999-01-01
Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.
Advanced Imaging Algorithms for Radiation Imaging Systems
Marleau, Peter
2015-10-01
The intent of the proposed work, in collaboration with University of Michigan, is to develop the algorithms that will bring the analysis from qualitative images to quantitative attributes of objects containing SNM. The first step to achieving this is to develop an indepth understanding of the intrinsic errors associated with the deconvolution and MLEM algorithms. A significant new effort will be undertaken to relate the image data to a posited three-dimensional model of geometric primitives that can be adjusted to get the best fit. In this way, parameters of the model such as sizes, shapes, and masses can be extracted for both radioactive and non-radioactive materials. This model-based algorithm will need the integrated response of a hypothesized configuration of material to be calculated many times. As such, both the MLEM and the model-based algorithm require significant increases in calculation speed in order to converge to solutions in practical amounts of time.
Evolutionary path control strategy for solving many-objective optimization problem.
Roy, Proteek Chandan; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin
2015-04-01
The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs. The central component of our algorithm is the use of a reference vector that helps simultaneously minimizing all the objectives of an MaOP. In doing so, EPCS employs a new fitness assignment strategy for survival selection. This strategy consists of two procedures and our algorithm applies them sequentially. It encourages a population of solutions to follow a certain path reaching toward the Pareto optimal front. The essence of our strategy is that it reduces the number of nondominated solutions to increase selection pressure in evolution. Furthermore, unlike previous work, EPCS is able to apply the classical Pareto-dominance relation with the new fitness assignment strategy. Our algorithm has been tested extensively on several scalable test problems, namely five DTLZ problems with 5 to 40 objectives and six WFG problems with 2 to 13 objectives. Furthermore, the algorithm has been tested on six CEC09 problems having 2 or 3 objectives. The experimental results show that EPCS is capable of finding better solutions compared to other existing algorithms for problems with an increasing number of objectives.
The Evolving Theory of Evolutionary Radiations.
Simões, M; Breitkreuz, L; Alvarado, M; Baca, S; Cooper, J C; Heins, L; Herzog, K; Lieberman, B S
2016-01-01
Evolutionary radiations have intrigued biologists for more than 100 years, and our understanding of the patterns and processes associated with these radiations continues to grow and evolve. Recently it has been recognized that there are many different types of evolutionary radiation beyond the well-studied adaptive radiations. We focus here on multifarious types of evolutionary radiations, paying special attention to the abiotic factors that might trigger diversification in clades. We integrate concepts such as exaptation, species selection, coevolution, and the turnover-pulse hypothesis (TPH) into the theoretical framework of evolutionary radiations. We also discuss other phenomena that are related to, but distinct from, evolutionary radiations that have relevance for evolutionary biology.
Probabilistic model-based approach for heart beat detection.
Chen, Hugh; Erol, Yusuf; Shen, Eric; Russell, Stuart
2016-09-01
Nowadays, hospitals are ubiquitous and integral to modern society. Patients flow in and out of a veritable whirlwind of paperwork, consultations, and potential inpatient admissions, through an abstracted system that is not without flaws. One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system. One longitudinal study reported an 88.8% rate of false alarms, with other studies reporting numbers of similar magnitudes. These false alarm rates lead to deleterious effects that manifest in a lower standard of care across clinics. This paper discusses a model-based probabilistic inference approach to estimate physiological variables at a detection level. We design a generative model that complies with a layman's understanding of human physiology and perform approximate Bayesian inference. One primary goal of this paper is to justify a Bayesian modeling approach to increasing robustness in a physiological domain. In order to evaluate our algorithm we look at the application of heart beat detection using four datasets provided by PhysioNet, a research resource for complex physiological signals, in the form of the PhysioNet 2014 Challenge set-p1 and set-p2, the MIT-BIH Polysomnographic Database, and the MGH/MF Waveform Database. On these data sets our algorithm performs on par with the other top six submissions to the PhysioNet 2014 challenge. The overall evaluation scores in terms of sensitivity and positive predictivity values obtained were as follows: set-p1 (99.72%), set-p2 (93.51%), MIT-BIH (99.66%), and MGH/MF (95.53%). These scores are based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity.
Model-based damage evaluation of layered CFRP structures
NASA Astrophysics Data System (ADS)
Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.
2015-03-01
An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.