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Evolutionary Computation, 2000. Proceedings of the 2000 Congress on

Date 16-19 July 2000

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  • Proceedings of the 2000 Congress on Evolutionary Computation

    Publication Year: 2000 , Page(s): 0_2 - xxvi
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  • Proceedings ASE 2000. Fifteenth IEEE International Conference on Automated Software Engineering

    Publication Year: 2000
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    Freely Available from IEEE
  • Author index

    Publication Year: 2000 , Page(s): 1585 - 1590
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  • Concentration, capacity and market power in an evolutionary labor market

    Publication Year: 2000 , Page(s): 1033 - 1040 vol.2
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    This paper reports on an experimental study of the relationship between job capacity, job concentration, and market power in the context of an agent-based computational model of a labor market. Job capacity is measured by the ratio of potential job openings to potential work offers, and job concentration is measured by the ratio of work suppliers to employers. For each experimental treatment, work suppliers and employers repeatedly seek preferred work-site partners based on continually updated expected utility, engage in work-site interactions modelled as prisoner's dilemma games, and evolve their work-site behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power View full abstract»

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  • Triggered hypermutation revisited

    Publication Year: 2000 , Page(s): 1025 - 1032 vol.2
    Cited by:  Papers (23)
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    With the emergence of standardized problem generators for dynamic problem environments, we are just starting to systematically measure the performance of different evolutionary-algorithm (EA) extensions against standard classes of problems. We revisit triggered hypermutation, one of the early and most successful implementations of EA's for dynamic environments. Using an implementation of this algorithm, we systematically evaluate the performance of triggered hypermutation on specific test problems across a range of values for the environmental change rate relative to the EA “time” measured in generations. We examine the results, identify a probable cause for the algorithm's behavior, and suggest some improvements to the algorithm View full abstract»

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  • Modeling and convergence analysis of distributed co-evolutionary algorithms

    Publication Year: 2000 , Page(s): 1276 - 1283 vol.2
    Cited by:  Papers (5)  |  Patents (7)
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    A theoretical foundation is presented for modeling and convergence analysis of distributed co-evolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a general model of distributed co-evolutionary algorithms. Convergence and convergence rate analyses are pursued for certain basic classes of objective functions. Also considered are relative computational delays of the centralized and distributed algorithms when they are implemented in a network environment View full abstract»

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  • Electricity market power: marginal cost and relative capacity effects

    Publication Year: 2000 , Page(s): 1048 - 1055 vol.2
    Cited by:  Papers (2)
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    Deregulation of electricity markets throughout the world requires that markets be constructed so as to insure fair and efficient prices. We investigate the use of a bilateral auction between agents as the market clearing mechanism in which the agents learn to bid/ask effectively using a genetic algorithm. Market Power Indexes are calculated for various configurations of relative capacity and production costs for the sellers in such a market and indications of the exercising of market power are sought. We found no statistically significant market power effect due to relative marginal costs or capacities. However, this absence of market power effects appears to be a consequence of the small number of sellers and the manner in which the standard genetic algorithm used to model seller learning actually prevents the sellers from exploiting their comparative advantages View full abstract»

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  • Dynamic rotation and partial visibility

    Publication Year: 2000 , Page(s): 1125 - 1131 vol.2
    Cited by:  Papers (3)
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    This article generalizes a previously presented dynamic fitness function with two different concepts, namely a coordinate rotation and the concept of partial visibility. Those concepts define different classes of test problems. A set of standard evolution strategies and genetic algorithms with and without hypermutation are tested on two of the dynamic problem classes. They give insight into certain properties of the presented concepts and dynamic optimization in general View full abstract»

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  • Cultural algorithms: concepts and experiments

    Publication Year: 2000 , Page(s): 1245 - 1251 vol.2
    Cited by:  Papers (8)
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    Evolutionary computation is a generic name given to the resolution of computational problems that are planned and implemented based on models of the evolutionary process. Most of the evolutionary algorithms that have been proposed follow biological paradigms and the concepts of natural selection, mutation and reproduction. There are, however, other paradigms which may be adopted in the creation of evolutionary algorithms. Several problems involving unstructured environments may be addressed from the point of view of cultural paradigms, which offer plenty of categories of models where one does not know all possible solutions to a problem - a very common situation in real life. This work applies the computational properties of cultural technology to the solution of a specific problem, adapted from the robotics literature. A test environment denoted the “Cultural Algorithms Simulator” was developed to allow anyone to learn more about the rather unconventional characteristics of a cultural technology View full abstract»

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  • Stereotyping: improving particle swarm performance with cluster analysis

    Publication Year: 2000 , Page(s): 1507 - 1512 vol.2
    Cited by:  Papers (73)
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    Individuals in the particle swarm population were “stereotyped” by cluster analysis of their previous best positions. The cluster centers then were substituted for the individuals' and neighbors' best previous positions in the algorithm. The experiments, which were inspired by the social-psychological metaphor of social stereotyping, found that performance could be generally improved by substituting individuals', but not neighbors', cluster centers for their previous bests View full abstract»

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  • A framework of fuzzy modeling using genetic algorithms with appropriate combination of evaluation criteria

    Publication Year: 2000 , Page(s): 1252 - 1259 vol.2
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    Fuzzy modeling is a method to describe nonlinear input-output relationships. Genetic algorithms (GAs) have been used with fuzzy modeling for identification of the structure of a fuzzy model and selection of input variables. Users often require fuzzy models that satisfy multiple evaluation criteria. Assignment of appropriate weights on these criteria is one of the key factors for good GA search. In order to give a guideline for assigning the degree of importance to each evaluation criterion for generating fuzzy models, we examined the characteristic of each evaluation criterion View full abstract»

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  • Dynamic distributed genetic algorithms

    Publication Year: 2000 , Page(s): 1132 - 1136 vol.2
    Cited by:  Papers (5)
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    Distributed populations in genetic algorithms can make the search more smart, in that local minima may be skipped. However, when the global population is divided into small sub-populations, the ability of these sub-populations to evolve is set back because of their relatively small sizes. In this paper, a new method to manage the distributed populations in evolution is introduced. A supervising subroutine observes all the sub-populations during evolution. The sizes of these sub-populations are dynamically changed according to their performance. Better sub-populations get more quotas of the total number of individuals, thus get more possibility to produce even better ones. This algorithm is illustrated with an example. Different policies of managing the sub-populations are compared and discussed. The main conclusion is that dynamical rearrangement of the global population can make the process of evolution faster and more stable View full abstract»

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  • Revisiting Bremermann's genetic algorithm. I. Simultaneous mutation of all parameters

    Publication Year: 2000 , Page(s): 1204 - 1209 vol.2
    Cited by:  Papers (2)
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    Hans Bremermann was one of the pioneers of evolutionary computation. Many of his early suggestions for designing evolutionary algorithms anticipated future inventions, including scaling mutations to be inversely proportional to the number of parameters in the problem, as well as many forms of recombination. This paper explores the gain in performance that occurs when Bremermann's original evolutionary algorithm (H.J. Bremermann et al., 1966) is extended to include the simultaneous mutation of every component in a candidate solution. Bremermann's original perspective was entirely “genetic”, where each component corresponded to a gene, and therefore multiple simultaneous changes were viewed as occurring with geometrically decreasing probability. Experiments indicate that a change in perspective to a “phenotypic” view, where all the components change at once, can lead to more rapid optimization on linear systems of equations View full abstract»

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  • Training neurocontrollers by local and evolutionary search

    Publication Year: 2000 , Page(s): 1558 - 1564 vol.2
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    Training of neural networks by local search such as gradient based algorithms could be difficult. This calls for the development of alternative training algorithms such as evolutionary search. However, training by evolutionary search often requires long computation time. The authors investigate the possibilities of reducing the time taken by combining the efforts of local search and evolutionary search. There are a number of approaches to combine these search strategies, but not all of them are successful. The paper provides a review of these approaches. Experimental results indicate that while the Baldwinian and the two-phase approaches are inefficient in improving the evolution process for difficult problems, the Lamarckian approach is able to speed up the training process. Moreover in the case where no local search method is appropriate for learning the desired task directly, the paper demonstrates that allowing the local search to learn another related task can assist the evolutionary search View full abstract»

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  • Misleading functions designed from alternation

    Publication Year: 2000 , Page(s): 1056 - 1063 vol.2
    Cited by:  Papers (1)
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    The paper proposes the design of difficult functions for a GA (genetic algorithm) where the deceptive attractor is at mid-distance from the global optimum. First, piecewise-linear trap functions of alternation are investigated. We consider alternation based distance to enable the ability of fitness distance correlation coefficient to predict GA behavior on such functions. Then, we generalize to any function by way of the derivative transformation applied on bit strings. These preliminary results support the following conjecture: derivative transforms are difficult problems, where competition occurs between complementary strings, and lead to misleading problems where crossover is an effective operator and competitors are at mid-distance from each other View full abstract»

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  • Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains

    Publication Year: 2000 , Page(s): 938 - 945 vol.2
    Cited by:  Patents (1)
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    Bayesian evolutionary algorithms (BEAs) are a probabilistic model of evolutionary computation for learning and optimization. Starting from a population of individuals drawn from a prior distribution, a Bayesian evolutionary algorithm iteratively generates a new population by estimating the posterior fitness distribution of parent individuals and then sampling from the distribution offspring individuals by variation and selection operators. Due to the non-homogeneity of their Markov chains, the convergence properties of the full BEAs are difficult to analyze. However, recent developments in Markov chain analysis for dynamic Monte Carlo methods provide a useful tool for studying asymptotic behaviors of adaptive Markov chain Monte Carlo methods including evolutionary algorithms. We apply these results to Investigate the convergence properties of Bayesian evolutionary algorithms with incremental data growth. We study the case of BEAs that generate single chains or have populations of size one. It is shown that under regularity conditions the incremental BEA asymptotically converges to a maximum a posteriori (MAP) estimate which is concentrated around the maximum likelihood estimate. This result relies on the observation that increasing the number of data items has an equivalent effect of reducing the temperature in simulated annealing View full abstract»

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  • Co-evolutionary dynamics on a deformable landscape

    Publication Year: 2000 , Page(s): 1284 - 1291 vol.2
    Cited by:  Papers (1)
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    In order to use co-evolutionary techniques successfully one needs to investigate the dynamics of co-evolution. Assuming an open-ended evolutionary process, it would be desirable to establish the necessary and sufficient conditions which lead to a kind of arms race where species continually adapt in response to one another. In this paper we present a model of competitive co-evolution which is intended to investigate these conditions. In our model, co-evolving species are modeled as points which are placed randomly on a uniform landscape which is deformed by the species. The impact a species induces on its surrounding is not immediate. Instead, the deformation follows the species after some latency period. Evolution is modeled as a simple hill climbing process of the species. We investigate different conditions and their impact on the evolutionary dynamics. Some lead to stasis, some lead to cyclic behavior and others lead to an arms race View full abstract»

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  • Expressing evolutionary computation, genetic programming, artificial life, autonomous agents and DNA-based computing in -calculus-revised version

    Publication Year: 2000 , Page(s): 1361 - 1368 vol.2
    Cited by:  Papers (1)
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    Genetic programming, autonomous agents, artificial life and evolutionary computation share many common ideas. They generally investigate distributed complex processes, perhaps with the ability to interact. It seems to be natural to study their behavior using process algebras, which were designed to handle distributed interactive systems. -calculus is a higher-order polyadic process algebra for resource bounded computation. It has been designed to handle autonomous agents, evolutionary computing, neural nets, expert systems, machine learning, and distributed interactive AI systems, in general. -calculus has a built-in cost-optimization mechanism allowing to deal with nondeterminism, incomplete and uncertain information. We express in -calculus several subareas of evolutionary computation, including genetic programming, artificial life, autonomous agents and DNA-based computing View full abstract»

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  • ClaDia: a fuzzy classifier system for disease diagnosis

    Publication Year: 2000 , Page(s): 1429 - 1435 vol.2
    Cited by:  Papers (4)
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    The paper describes ClaDia, a learning classifier system applied to the Wisconsin breast cancer data set, using a fuzzy representation of the rules, a median based fuzzy combination rule, and separate subpopulations for each class. The system achieves a classification rate of over 90%, for many sets of system parameter values View full abstract»

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  • MyEnglishTeacher-an evolutionary Web-based, multi-agent environment for academic English teaching

    Publication Year: 2000 , Page(s): 1345 - 1353 vol.2
    Cited by:  Papers (3)
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    We describe our research on building a free, evolutionary, Internet-based, agent-based, long-distance teaching environment for academic English. Web English teaching environments are few, and mostly they imply a fee. However, none of them considers the challenges the non-native English-speaking academic has to face. We describe some of the design and implementation aspects of the system prototype, focusing especially on the evolutionary, adaptive features, and only marginally on the pedagogical issues involved View full abstract»

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  • An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization

    Publication Year: 2000 , Page(s): 986 - 993 vol.2
    Cited by:  Papers (9)
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    This paper we describe a method for improving genetic algorithm based optimization using reduced models. The main idea is to maintain a large sample of the points encountered in the course of the optimization divided into clusters. Least squares quadratic approximations are periodically formed of the entire sample as well as the big enough clusters. These approximations are used as a reduced model to compute cheap approximations of the fitness function through a two phase approach in which the point is first classified (into potentially feasible, infeasible or unevaluable) and then its fitness is computed accordingly. We then use the reduced models to speedup the GA optimization by making the genetic operators such as mutation and crossover more informed. The proposed approach is particularly suitable for search spaces with expensive evaluation functions, such as those that arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly speed up the GA optimizer View full abstract»

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  • Cultural algorithms in dynamic environments

    Publication Year: 2000 , Page(s): 1513 - 1520 vol.2
    Cited by:  Papers (3)
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    Reasoning about environmental change in dynamic environments is a key factor in predicting an agent's next move in those environments. Cultural Algorithms provide a mechanism to reason about environmental dynamics. In this study, cultural algorithms show encouraging results when applied to environments where the problem is to find the highest peak in a multidimensional landscape, where the peaks are moving over time. Here, we use De Jong's environmental dynamics simulator and observe how tracking of change is affected by the frequency of change, and the magnitude for both a self-adaptive EP and a cultured EP version. It is shown that the cultured system is less sensitive to the environmental changes and outperforms the self-adaptive EP system particularly when the frequency of change becomes very large View full abstract»

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  • Dynamics of a distance-based population diversity measure

    Publication Year: 2000 , Page(s): 1002 - 1009 vol.2
    Cited by:  Papers (5)
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    We study a class of steady-state genetic algorithms where, at each time step, two parents are selected to produce a child which then replaces one member of the population at the next time step. We consider the finite-population case. A general crossover and mutation operation are defined, as well as a genomic distance between individuals. Certain specific properties are required to hold for such operations and distance functions, and we present examples of crossover operations, mutation operations, and distance functions which meet the requirements. We then define the sum over all pairwise population distances as a measure of the diversity of a population and consider the time evolution of the expected diversity of a population. We show conditions where, under uniform, independent selection of parents and the individual to be replaced, the expected diversity monotonically approaches a fixed point. For this case we calculate an explicit formula for the expected diversity at each time step View full abstract»

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  • Modeling the spread of antibiotic resistance

    Publication Year: 2000 , Page(s): 1152 - 1159 vol.2
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    This paper describes a stochastic implementation of Austin et al.'s (1999) model of the spread of antibiotic resistance in a population of fixed size under varying conditions of antibiotic use. The population is divided into sub-groups: individuals colonized by commensal bacteria and an uncolonized group. The colonized group is further divided according to whether the commensal bacteria are sensitive or resistant to antibiotics. This study uses Monte Carlo techniques to model the dynamics of the evolution of the antibiotic resistant population, a study that cannot be done in the original model. The Monte Carlo approach allows the investigation of the transient dynamics of the spread of resistance, the effects of finite (especially small) populations and the interaction of model parameters View full abstract»

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  • Information integration and red queen dynamics in coevolutionary optimization

    Publication Year: 2000 , Page(s): 1260 - 1267 vol.2
    Cited by:  Papers (3)
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    Coevolution has been used as optimization technique both successfully and unsuccessfully. Successful optimization shows integration of information at the individual level over many fitness evaluation events and over many generations. Alternative outcomes of the evolutionary process, e.g. red queen dynamics or speciation, prevent such integration. Why coevolution leads to integration of information or to alternative evolutionary outcomes is generally unclear. We study coevolutionary optimization of the density classification task in cellular automata in a spatially explicit, two-species model. We find optimization at the individual level, i.e. evolution of cellular automata that are good density classifiers. However, when we globally mix the populations, which prevents the formation of spatial patterns, we find typical red queen dynamics in which cellular automata classify all cases to a single density class regardless their actual density. Thus, we get different outcomes of the evolutionary process dependent on a small change in the model. We compare the two processes leading to the different outcomes in terms of the diversity of the two populations at the level of the genotype and at the level of the phenotype View full abstract»

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