Pseudo-hill climbing genetic algorithm (PHGA) for function optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

In general, one of the shortcomings in GAs as search methods is their lack of local search ability. The main objective of this paper is to combine the ideas of Simplex method with the genetic algorithms (GAs). In order to give a hill-climbing ability to the conventional GAs, like neural networks, we propose a new GA named PHGA Genetic Algorithm (PHGA) for function optimization. Computer simulation results using De Jong's five-function test bed are shown. According to our simulation, all of the results by the proposed PHGA are better than those by the conventional GAs.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages713-716
Number of pages4
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993 Dec 1
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Hagiwara, M. (1993). Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. In Proceedings of the International Joint Conference on Neural Networks (pp. 713-716). (Proceedings of the International Joint Conference on Neural Networks; Vol. 1). Publ by IEEE.