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Genetic algorithms and evolution.

B H Sumida1, A I Houston, J M McNamara

  • 1Department of Zoology, Oxford University, U.K.

Journal of Theoretical Biology
|November 7, 1990
PubMed
Summary
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This study modified the genetic algorithm (GA) by dividing populations, enhancing its efficiency in finding optimal solutions. Subdividing populations improved GA performance on complex problems, including bird song evolution.

Area of Science:

  • Computational intelligence
  • Evolutionary computation
  • Bio-inspired algorithms

Background:

  • The genetic algorithm (GA) is an optimization technique inspired by natural selection.
  • Investigating the efficiency of natural selection as a search procedure is crucial for algorithm development.
  • Epistatic interactions present challenges for optimization algorithms.

Purpose of the Study:

  • To investigate the efficiency of natural selection as a search procedure using a modified genetic algorithm.
  • To evaluate the impact of subdividing populations into semi-isolated demes on GA performance.
  • To compare GA performance against dynamic programming in a biological model.

Main Methods:

  • Modification of Holland's genetic algorithm by introducing semi-isolated demes.

Related Experiment Videos

  • Application of the modified GA to a fitness landscape with multiple local optima.
  • Application of the modified GA to a model of bird song evolution previously analyzed with dynamic programming.

    • Main Results:

    • The modified genetic algorithm successfully identified the global optimum in a complex fitness landscape.
    • Subdividing the population significantly improved the success rate of the genetic algorithm.
    • The genetic algorithm evolved to the optimal policy in the bird song model, matching dynamic programming results.

    Conclusions:

    • Subdividing populations in genetic algorithms enhances their efficiency as search procedures, particularly in landscapes with local optima.
    • Modified genetic algorithms are effective for optimizing complex systems, including biological models with epistatic interactions.
    • This research provides insights into the evolutionary mechanisms driving efficient search in natural and artificial systems.