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Adapting operator settings in genetic algorithms.

A Tuson1, P Ross

  • 1Department of Artificial Intelligence, University of Edinburgh, U.K. andrewt@dai.ed.ac.uk

Evolutionary Computation
|February 18, 1999
PubMed
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Adapting genetic algorithm operator settings during a run can improve performance. This study investigates two adaptation methods on theoretical and flowshop sequencing problems, finding applicability depends on problem assumptions.

Area of Science:

  • Computational intelligence
  • Operations research

Background:

  • Genetic algorithms (GAs) typically use fixed operator settings.
  • Operator settings may need dynamic adjustment as fitness landscape changes.

Purpose of the Study:

  • Investigate the impact of adaptive operator settings in genetic algorithms.
  • Evaluate two adaptation methods on diverse problem types.

Main Methods:

  • Implemented two operator adaptation techniques.
  • Tested methods on theoretical problems and the flowshop sequencing problem.

Main Results:

  • Operator adaptation effectiveness is contingent on specific problem characteristics.
  • Performance gains are observed when underlying assumptions for adaptation are met.

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Conclusions:

  • Adaptive operator settings can enhance genetic algorithm performance.
  • Problem-specific assumptions must be considered for successful operator adaptation.