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Island model genetic algorithms and linearly separable problems

  • Darrell Whitley
  • Soraya Rana
  • Robert B. Heckendorn
Problem Structure and Fitness Landscapes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)

Abstract

Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems.

Keywords

Genetic Algorithm Single Population Finite Population Separable Problem Island Model 
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 1997

Authors and Affiliations

  • Darrell Whitley
    • 1
  • Soraya Rana
    • 1
  • Robert B. Heckendorn
    • 1
  1. 1.Department of Computer ScienceColorado State UniversityFort CollinsUSA

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