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Neutral Networks and Evolvability with Complex Genotype-Phenotype Mapping

  • Tom Smith
  • Phil Husbands
  • Michael O’Shea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2159)

Abstract

In this paper, we investigate a neutral epoch during an optimisation run with complex genotype-to-fitness mapping. The behaviour of the search process during neutral epochs is of importance for evolutionary r obotics and other artificial-life approaches that evolve problem solutions; recent work has argued that evolvability may change during these epochs. We investigate the distribution of offspring fitnesses from the best individuals of each generation in a population-based genetic algorithm, and see no trends towards higher probabilities of producing higher fitness offspring, and no trends towards higher probabilities of not producing lower fitness offspring. A second experiment in which populations from across the neutral epoch are used as initial populations for the genetic algorithm, shows no difference between the populations in the number of generations required to produce high fitness. We conclude that there is no evidence for change in evolvability during the neutral epoch in this optimisation run; the population is not doing anything “useful” during this period.

Keywords

Genetic Algorithm Search Space Recurrent Neural Network Good Individual Transmission Function 
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 2001

Authors and Affiliations

  • Tom Smith
    • 1
    • 2
  • Phil Husbands
    • 1
    • 3
  • Michael O’Shea
    • 1
    • 2
  1. 1.Centre for Computational Neuroscience and Robotics (CCNR)BrightonUK
  2. 2.School of Biological SciencesBrightonUK
  3. 3.School of Cognitive and Computing SciencesUniversity of SussexBrightonUK

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