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Online Discovery of Search Objectives for Test-Based Problems.

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Evolutionary Computation
|March 9, 2016
PubMed
Summary

Scalar fitness in competitive coevolutionary algorithms can cause premature convergence. This study introduces disco, a method that groups tests to define distinct skills, improving evolutionary search performance.

Keywords:
Coevolutionmulti-objective evolutionary computationsearch drivertest-based problems

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Competitive coevolutionary algorithms determine solution fitness via interactions with multiple tests.
  • Scalar fitness aggregates test outcomes, imposing a complete order but potentially masking diverse solution capabilities.

Purpose of the Study:

  • To provide theoretical evidence that scalar fitness can lead to premature convergence in test-based problems.
  • To propose a novel method, disco, to mitigate premature convergence by addressing the limitations of scalar fitness.

Main Methods:

  • The disco method automatically identifies groups of tests where candidate solutions exhibit similar behaviors, defining distinct 'skills'.
  • These test groups generate derived objectives, guiding the search algorithm in a multi-objective manner.

Main Results:

  • The proposed disco approach significantly outperforms conventional two-population coevolution on several benchmark test-based problems.
  • Disco effectively captures skill variations among candidate solutions, unlike scalar fitness measures.

Conclusions:

  • Disco offers an efficient and generic countermeasure against premature convergence in evolutionary and coevolutionary algorithms.
  • This method enhances search performance for problems with aggregating fitness functions by leveraging multi-objective optimization.