This book constitutes the refereed proceedings of the 7th International Conference on Parallel Problem Solving from Nature,PPSN 2002, held in Granada, Spain in September 2002. The 90 revised full papers presented were carefully reviewed and selected from 181 submissions. The papers are organized in topical sections on evolutionary algorithms theory, representation and codification, variation operators, evolutionary techniques and coevolution, multiobjective optimization, new techniques for evolutionary algorithms, hybrid algorithms, learning classifier systems, implementation of evolutionary algorithms, applications, and cellular automata and ant colony optimization.
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Dynamic optimization is distinguished from static optimization in that in the former, the objective function is not constant but varies with time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dynamic traffic modeling, cumulative mutation strength adaptation, edit distance diversity, decompositional bias, functional model proteins, expected optimization time, intended solution instance, list scheduler algorithm, negative correlation learning, cellular evolutionary algorithms, new direct representation, separate fitness functions, start expression genes, dynamic restart strategies, resampling ratio, priori tour, uniformly scaled problems, lmax problem, operation oik, root identification problem, gradual landscapes, evolutionary algorithm machine, optimal mutation strength, extinctive selection, mutation serial Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, Springer-Verlag Berlin Heidelberg, Computation Conference, New York, San Francisco, San Mateo, Springer Verlag, University of Michigan, Oxford University Press, Multimeme Algorithm, John Wiley, European Conference, Fuzzy Classifier System, Ann Arbor, Kluwer Academic Publishers, Mean Stdev, University of the West of England, Introduction Evolutionary, Libre de Bruxelles, University of Illinois, Academic Press, Cambridge University Press, European Journal of Operational Research, Even Parity New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Dynamic optimization is distinguished from static optimization in that in the former, the objective function is not constant but varies with time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dynamic traffic modeling, cumulative mutation strength adaptation, edit distance diversity, decompositional bias, functional model proteins, expected optimization time, intended solution instance, list scheduler algorithm, negative correlation learning, cellular evolutionary algorithms, new direct representation, separate fitness functions, start expression genes, dynamic restart strategies, resampling ratio, priori tour, uniformly scaled problems, lmax problem, operation oik, root identification problem, gradual landscapes, evolutionary algorithm machine, optimal mutation strength, extinctive selection, mutation serial Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, Springer-Verlag Berlin Heidelberg, Computation Conference, New York, San Francisco, San Mateo, Springer Verlag, University of Michigan, Oxford University Press, Multimeme Algorithm, John Wiley, European Conference, Fuzzy Classifier System, Ann Arbor, Kluwer Academic Publishers, Mean Stdev, University of the West of England, Introduction Evolutionary, Libre de Bruxelles, University of Illinois, Academic Press, Cambridge University Press, European Journal of Operational Research, Even Parity New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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