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An introduction to simulated evolutionary optimization.

D B Fogel1

  • 1Nat. Selection Inc., La Jolla, CA.

IEEE Transactions on Neural Networks
|January 1, 1994
PubMed
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Simulated evolution uses computer simulations of natural processes for optimization. Three main methods—genetic algorithms, evolution strategies, and evolutionary programming—offer powerful alternatives to classical optimization techniques for complex problems.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Theory

Background:

  • Natural evolution is a population-based optimization process.
  • Simulated evolution employs stochastic optimization techniques inspired by natural evolution.
  • These techniques can surpass classical methods in solving complex real-world problems.

Purpose of the Study:

  • To describe the development of simulated evolution research over the past 35 years.
  • To review recent advancements in the field.
  • To differentiate the three main avenues of simulated evolution.

Main Methods:

  • Overview of genetic algorithms, emphasizing chromosomal operators.
  • Explanation of evolution strategies, focusing on individual behavioral changes.

Related Experiment Videos

  • Description of evolutionary programming, highlighting species-level behavioral changes.

    • Main Results:

    • The study outlines the historical progression of simulated evolution methodologies.
    • It details the distinct focuses of genetic algorithms, evolution strategies, and evolutionary programming.
    • Recent research efforts in these areas are summarized.

    Conclusions:

    • Simulated evolution offers a robust set of optimization tools.
    • The three primary approaches (genetic algorithms, evolution strategies, evolutionary programming) cater to different aspects of evolutionary processes.
    • These methods provide valuable alternatives for tackling challenging optimization tasks.