Multi-points evolution strategy and designing emergent parameter tuning rule using genetic programming

Minoru Kanemasa, Eitaro Aiyoshi

Research output: Contribution to journalArticlepeer-review

Abstract

Modern heuristic optimization algorithms developed in '90s have been a particular focus of attention because of their simplicity, easy software implementation, and moreover, the interesting phenomena that their performance emerged from the interactions among the particles. In this paper, we see that we can get emergent performance as an optimization algorithm by increasing the number of particles on Evolution Strategy. Considering that, we try to increase the interactions among the particles in order to get better performance. We define parameter tuning rule designing as an optimization problem, and use Genetic Programming to find those for Evolution Strategy. In addition, we evaluate the generated tuning rules using statistical tests and several benchmarks to verify that the proposed methods and the generated rules are effective ones.

Original languageEnglish
Pages (from-to)321-330
Number of pages10
JournalIEEJ Transactions on Electronics, Information and Systems
Volume135
Issue number3
DOIs
Publication statusPublished - 2015 Mar 1

Keywords

  • Algorithm tuner
  • Evolution strategy
  • Genetic programming
  • Heuristic algorithms

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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