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Related Experiment Videos

On the effectiveness of crossover in simulated evolutionary optimization

D B Fogel1, L C Stayton

  • 1Natural Selection, Inc., La Jolla, CA 92037.

Bio Systems
|January 1, 1994
PubMed
Summary

Simulated evolution for optimization problems shows genetic models using crossover are more effective than behavioral models relying on mutation. This study empirically assesses these claims across various response surfaces.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Renewed interest exists in simulated evolution for complex optimization challenges.
  • Simulated evolution approaches are broadly categorized into genetic models (chromosomes, operators) and behavioral models (individuals, adaptation).
  • Recent assertions propose genetic models with recombination outperform mutation-reliant behavioral models for function optimization.

Purpose of the Study:

  • To empirically evaluate recent claims comparing the efficiency and effectiveness of genetic and behavioral models in simulated evolution.
  • To assess the performance of recombination operators (e.g., crossover) versus mutation-only strategies in function optimization.
  • To test these models across a diverse set of response surfaces.

Main Methods:

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  • Empirical assessment of simulated evolution models on various response surfaces.
  • Comparison of genetic models emphasizing genetic operators (like crossover) against behavioral models focusing on adaptation and diversity.
  • Performance evaluation based on efficiency and effectiveness in function optimization.

Main Results:

  • The study empirically tested claims regarding the superiority of genetic models with crossover over mutation-based behavioral models.
  • Performance was analyzed across a wide spectrum of response surfaces to validate the claims.
  • Results provide empirical evidence on the comparative effectiveness of different simulated evolution strategies.

Conclusions:

  • The empirical assessment provides insights into the relative strengths of genetic and behavioral models in simulated evolution.
  • Findings contribute to understanding the efficacy of crossover versus mutation for function optimization.
  • This research informs the selection of appropriate simulated evolution strategies for difficult optimization problems.