Genetic Algorithms with Self-Organized Criticality for Dynamic Optimization Problems

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Genetic Algorithms with Self-Organized Criticality for Dynamic Optimization Problems (2005)

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by Renato Tinós , Shengxiang Yang

Venue:	In Proceedings of the 2005 Congress on Evolutionary Computation
Citations:	2 - 0 self

BibTeX

@INPROCEEDINGS{Tinós05geneticalgorithms,
    author = {Renato Tinós and Shengxiang Yang},
    title = {Genetic Algorithms with Self-Organized Criticality for Dynamic Optimization Problems},
    booktitle = {In Proceedings of the 2005 Congress on Evolutionary Computation},
    year = {2005},
    pages = {2816--2823}
}

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Abstract

Abstract- This paper proposes a genetic algorithm (GA) with random immigrants for dynamic optimization problems where the worst individual and its neighbours are replaced every generation. In this GA, the individuals interact with each other and, when their fitness is close, as in the case where the diversity level is low, one single replacement can affect a large number of individuals. This simple approach can take the system to a kind of self-organization behavior, known as Self-Organized Criticality (SOC), which is useful to maintain the diversity of the population in dynamic environments and hence allows the GA to escape from local optima when the problem changes. The experimental results show that the proposed GA presents the phenomenon of SOC. 1

Keyphrases

genetic algorithm dynamic optimization problem self-organized criticality problem change self-organization behavior local optimum diversity level single replacement large number dynamic environment experimental result random immigrant simple approach

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