Neutral networks in an evolutionary robotics search space

  • Smith T
  • Husbands P
  • O'Shea M
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Abstract

Recent work has argued for the importance of non-adaptive neutral
evolution in optimisation over difficult search landscapes. We show that
the search process underlying a difficult evolutionary robotics problem
does indeed show phases of neutral evolution. The noise in evaluated
fitness of a single genotype is shown to be able to account for the
variance in fitness across a long period of the evolutionary run. We
further show that the population moves significantly in genotype space
during this neutral phase, possibly increasing in divergence. Finally,
we investigate the probabilities of mutating to a higher fitness, above
the neutral plateau, and find no evidence for a significant upward trend
in these probabilities before the crucial mutations actually occurred

Author-supplied keywords

  • Biological neural networks
  • Biological system modeling
  • Biology computing
  • Cognitive robotics
  • Extraterrestrial measurements
  • GasNet
  • Measurement standards
  • Neural networks
  • Orbital robotics
  • Robot control
  • Sampling methods
  • evolutionary computation
  • evolutionary robotics search space
  • evolvability
  • fitness landscapes
  • gaseous neuromodulation
  • gaseous signalling mechanism
  • neural nets
  • neural networks
  • optimisation problems
  • random sampling
  • robots
  • search difficulty metrics
  • search problems
  • search spaces
  • skewed fitness distributions

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Authors

  • T. Smith

  • P. Husbands

  • M. O'Shea

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