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Maintaining genetic diversity in genetic algorithms through co-evolution

  • Jason Morrison
  • Franz Oppacher
Genetic Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1418)

Abstract

This paper presents a systematic approach to co-evolution that allows concise and unified expression of all types of symbiotic relationships studied in ecology. The resulting Linear Model of Symbiosis can be easily added to any regular Genetic Algorithm. Our model helps prevent premature convergence to a local optimum by maintaining the genetic diversity in a population. Our experiments show that co-evolutionary Genetic Algorithms outperform regular Genetic Algorithms on some difficult problems including one (Holland's Royal Road function) which was specifically designed to highlight the strengths of a regular Genetic Algorithm.

Keywords

Genetic Algorithm Linear Connection Connection Strength Absolute Fitness High Fitness Level 
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

  • Jason Morrison
    • 1
  • Franz Oppacher
    • 1
  1. 1.Intelligent Systems Lab School of Computer ScienceCarleton UniversityOttawa

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