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Efficient and scalable Pareto optimization by evolutionary local selection algorithms.

F Menczer1, M Degeratu, W N Street

  • 1Management Sciences Department, University of Iowa, Iowa City 52242, USA. filippo-menczer@uiowa.edu

Evolutionary Computation
|June 8, 2000
PubMed
Summary

Local selection, an evolutionary computation method, maintains diversity efficiently for multiobjective problems. The ELSA algorithm using local selection significantly outperforms other evolutionary algorithms in experiments.

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Area of Science:

  • Evolutionary Computation
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Local selection is a novel evolutionary computation scheme.
  • It compares individual fitnesses to a threshold for reproduction.
  • This method maintains diversity and is efficient for parallel processing.

Purpose of the Study:

  • Introduce ELSA, an evolutionary algorithm utilizing local selection.
  • Evaluate ELSA's performance on multiobjective problems.
  • Compare ELSA against established evolutionary algorithms.

Main Methods:

  • Implemented local selection within the ELSA algorithm.
  • Applied ELSA to a multimodal graph search problem.
  • Tested ELSA on two Pareto optimization problems.

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Main Results:

  • ELSA demonstrated superior performance across all tested multiobjective problems.
  • Local selection proved effective in maintaining population diversity.
  • The algorithm showed significant improvements over existing methods.

Conclusions:

  • ELSA, powered by local selection, is a highly effective algorithm for multiobjective optimization.
  • Local selection offers an efficient and scalable approach for evolutionary computation.
  • Further research into distributed applications and parameter tuning is warranted.