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Redundant representations in evolutionary computation.

Franz Rothlauf1, David E Goldberg

  • 1Department of Information Systems 1, University of Mannheim, Schloss, D-68131 Mannheim, Germany. rothlauf@uni-mannheim.de

Evolutionary Computation
|November 25, 2003
PubMed
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Redundant representations impact genetic and evolutionary algorithms. Synonymously redundant representations can improve performance by overrepresenting optimal solutions, with models predicting necessary population size and generations.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Biology

Background:

  • Redundant representations, where genotype count exceeds phenotype count, are crucial in genetic and evolutionary algorithms.
  • A key distinction exists between synonymously redundant (similar genotypes for one phenotype) and non-synonymously redundant representations.

Purpose of the Study:

  • To analyze the influence of redundant representations on the performance of genetic and evolutionary algorithms.
  • To develop theoretical models for synonymously redundant representations to predict performance metrics.

Main Methods:

  • Theoretical modeling of synonymously redundant representations.
  • Analysis of selectorecombinative genetic algorithms (GAs) with modified initial populations.
  • Empirical validation using binary trivial voting mapping and real-valued link-biased encoding.

Related Experiment Videos

Main Results:

  • Non-synonymously redundant representations hinder evolutionary search performance.
  • Synonymously redundant representations' performance depends on initial population modification.
  • Theoretical models predict population size and convergence time as O(2(kr)/r), where kr is redundancy order and r is genotypic building blocks.
  • Uniformly redundant representations do not alter GA behavior; performance increases with overrepresentation (increasing r).

Conclusions:

  • Synonymously redundant representations can enhance GA performance, particularly when overrepresenting optimal solutions.
  • Non-uniformly redundant representations are beneficial only with prior knowledge of the optimal solution.
  • Developed models accurately predict empirical results for population sizing and convergence time.