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A genetic system based on simulated crossover of sequences of two-bit genes

THEORETICAL COMPUTER SCIENCE 2006年第3期367卷 324-335页

作者： Carpentieri, Marco Univ Salerno Dipartimento Fis ER Caianiello DF I-84100 Salerno Italy

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. (c) 2006 Elsevier B.V. All rights reserved.

关键词： marginal distribution genetic algorithms max-cut problem

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A genetic System Based on Simulated Crossover: Stability Analysis and Relationships with Neural Nets

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EVOLUTIONARY COMPUTATION 2009年第2期17卷 167-201页

作者： Carpentieri, Marco Univ Basilicata Dept Math & Comp Sci I-85100 Potenza Italy

We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound oil population size is exhibited stating that the behavior of the finite population system, in the case of sufficiently large sizes, can be approximated by the behavior of the corresponding infinite population system. Assumptions on fitness and individual chromosomes are provided implying that the behavior of the finite population genetic system remains consistent with the behavior of the associated infinite population genetic system for suitably long trajectories. The attractors (with binary components) of the infinite Population genetic system are characterized as equilibrium points of a discrete (neural network) system that call be considered as a variant of a Hopfield's network;it is shown that the fitness is a Lyapunov function for the variant of the discrete Hopfield's net. Our main result can be summarized by stating that the relation between marginal distribution genetic systems and neural nets is much more general than that already shown elsewhere for other simpler models.

关键词： genetic algorithms marginal distribution genetic algorithms estimation of distribution algorithms infinite population genetic modeling Hopfield's neural networks

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A genetic model based on simulated crossover of quaternary genes for quadratic fitness 06

A genetic model based on simulated crossover of quaternary g...

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8th Annual genetic and Evolutionary Computation Conference

作者： Carpentieri, Marco Fedullo, Vito Univ Salerno Dipartimento Matemat & Informat Fisciano SA Italy

ISBN: (纸本)9781595931863

We present a genetic model based on simulated recombination of fixed sequences of quaternary genes, (assuming four distinct forms, or alleles). States and dynamics of the infinite population genetic system, represented by the model, are derived for quadratic fitness functions. The bivariate marginal distribution genetic algorithm, devised to simulate the system, is experimentally compared with the univariate marginal distribution genetic algorithm with bit-based simulated crossover (in the case of infinite populations) for the MAX - CUT problem.

关键词： marginal distribution genetic algorithms

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