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Replacement strategies to preserve useful diversity in steady-state genetic algorithms

Authors:

Francisco Herrera,

José Ramón CanoAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 178, Issue 23

Pages 4421 - 4433

https://doi.org/10.1016/j.ins.2008.07.031

Published: 01 December 2008 Publication History

Abstract

In this paper, we propose a replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal tries to replace an individual in the population with worse values for these two features. In this way, the diversity of the population becomes increased and the quality of the solutions gets better, thus preserving high levels of useful diversity. Experimental results show the proposed replacement strategy achieved significant performance for problems with different difficulties, with regards to other replacement strategies presented in the literature.

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Index Terms

Replacement strategies to preserve useful diversity in steady-state genetic algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Theory of computation
  1. Models of computation
    1. Probabilistic computation

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 178, Issue 23

December, 2008

167 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2008.

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Elsevier Science Inc.

United States

Publication History

Published: 01 December 2008

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https://dl.acm.org/doi/10.1007/s10207-024-00964-3
Al-Eiadeh MAbdallah M(2024) GeniGraphComputers and Security10.1016/j.cose.2024.103927144:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103927
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