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A General Model of Co-evolution for Genetic Algorithms

  • Jason Morrison
  • Franz Oppacher

Abstract

Compared with natural systems, Genetic Algorithms have a limited adaptive capacity, i.e. they get quite frequently trapped at local optima and they are poor at tracking moving optima in dynamic environments. This paper describes a general, formal model of co-evolution, the Linear Model of Symbiosis, that allows for the concise, unified expression of all types of co-evolutionary relations studied in ecology. Experiments on several difficult problems support our assumption that the addition of the Linear Model of Symbiosis to a canonical Genetic Algorithm can remedy the above shortcomings.

Keywords

Genetic Algorithm Artificial Life Linear Connection Connection Strength Absolute Fitness 
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 Wien 1999

Authors and Affiliations

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

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