Optimization of cost functions using evolutionary algorithms with local learning and local search

Frederico G. Guimarães*, Felipe Campelo, Hajime Igarashi, David A. Lowther, Jaime A. Ramírez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Evolutionary algorithms can benefit from their association with local search operators, giving rise to hybrid or memetic algorithms. The cost of the local search may be prohibitive, particularly when dealing with computationally expensive functions. We propose the use of local approximations in the local search phase of memetic algorithms for optimization of cost functions. These local approximations are generated using only information already collected by the algorithm during the evolutionary process, requiring no additional evaluations. The local search improves some individuals of the population, hence speeding up the overall optimization process. We investigate the design of a loudspeaker magnet with seven variables. The results show the improvement achieved by the proposed combination of local learning and search within evolutionary algorithms.

Original languageEnglish
Pages (from-to)1641-1644
Number of pages4
JournalIEEE Transactions on Magnetics
Volume43
Issue number4
DOIs
Publication statusPublished - 1 Apr 2007

Keywords

  • Evolutionary algorithms
  • Hybrid methods
  • Memetic algorithms (MAs)

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