Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A Genetic Model: Analysis and Application to MAXSAT
Evolutionary Computation
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Evolutionary Computation
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FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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We introduce a genetic model based on simulated crossover of fixed sequences of two-bit genes. Results are(1)a lower bound on population size is exhibited such that a transition takes the stochastic finite population genetic system near the next state of the deterministic infinite population genetic system (provided both begin in the same state); (2)states and dynamics of the deterministic infinite population genetic system are derived for arbitrary (finite) fitness functions (expressed in terms of multivariate polynomials); (3)in the case of quadratic fitness defined by weight matrices with m nonnull entries it is shown that each state transition can be implemented in time O(m+l), where l is the chromosome length; (4)the genetic algorithm (implementing the proposed infinite population system) is experimentally compared with the infinite population genetic algorithm with bit-based simulated crossover for the max-cut problem; the results show that the extension to sequences of genes with four alleles is useful to improve performances.