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K L Mills1, J J Filliben2, A L Haines3

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Evolutionary Computation
|September 26, 2014
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Summary
This summary is machine-generated.

Optimizing genetic algorithm (GA) parameters is challenging. This study identifies crossover, mutation rate, and population size as key factors for GA success, offering crucial insights for evolutionary computation.

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Genetic algorithmsorthogonal fractional factorial experiment designsensitivity analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Optimization

Background:

  • Optimizing control parameters for genetic algorithms (GAs) is a persistent challenge in evolutionary computation.
  • Conflicting evidence exists in the literature regarding the impact and optimal settings of various GA parameters.

Purpose of the Study:

  • To develop and apply an experimental design and analysis method for determining the relative importance and effective settings of GA control parameters.
  • To identify the most influential parameters for a classic binary-encoded genetic algorithm.

Main Methods:

  • Designed and executed a comprehensive experiment across 60 numerical optimization problems.
  • Analyzed the influence of seven key genetic algorithm control parameters: crossover, mutation rate, population size, rerandomization point, elite selection, selection method, and chromosome precision.

Main Results:

  • Crossover was found to be the most significant factor influencing GA success.
  • Mutation rate and population size were the next most important parameters.
  • Selection method and chromosome precision had the least influence on performance.

Conclusions:

  • The study provides a robust method for parameter tuning in genetic algorithms.
  • The identified parameter importance offers practical guidance for engineers using GAs, particularly in complex systems like cloud computing.
  • Effective GA parameter settings can enhance the use of GAs as design tools for identifying system failure scenarios.