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Modelling genetic algorithms: From Markov chains to dependence with complete connections

  • Alexandru Agapie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

The paper deals with homogeneous stochastic models of the binary, finite-population Genetic Algorithm. Previous research brought the Markov chain analysis up to sufficient convergence conditions for the elitist case. In addition, we point out a condition that is both necessary and sufficient, for the general case convergence. Next, we present an example of algorithm, which is globally convergent yet not elitist. Considering this type of Markov chain analysis reached its end, we indicate another type of random systems — with complete connections — promising better results in real Genetic Algorithms modelling.

Keywords

Genetic Algorithm Markov Chain Simple Genetic Algorithm Recurrent State Markov Chain Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Alexandru Agapie
    • 1
  1. 1.Computational Intelligence Lab.National Institute of MicrotechnologyBucharestRomania

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