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
Volume1
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Other

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

Fingerprint

Genetic algorithms
Local search (optimization)
Neural networks
Computer simulation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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

Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. / Hagiwara, Masafumi.

Proceedings of the International Joint Conference on Neural Networks. Vol. 1 Publ by IEEE, 1993. p. 713-716.

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

Hagiwara, M 1993, Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. in Proceedings of the International Joint Conference on Neural Networks. vol. 1, Publ by IEEE, pp. 713-716, Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3), Nagoya, Jpn, 93/10/25.
Hagiwara M. Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. In Proceedings of the International Joint Conference on Neural Networks. Vol. 1. Publ by IEEE. 1993. p. 713-716
Hagiwara, Masafumi. / Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. Proceedings of the International Joint Conference on Neural Networks. Vol. 1 Publ by IEEE, 1993. pp. 713-716
@inproceedings{92b97c54a20c4ec9b6016db6c9b6d8ad,
title = "Pseudo-hill climbing genetic algorithm (PHGA) for function optimization",
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.",
author = "Masafumi Hagiwara",
year = "1993",
language = "English",
isbn = "0780314212",
volume = "1",
pages = "713--716",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Publ by IEEE",

}

TY - GEN

T1 - Pseudo-hill climbing genetic algorithm (PHGA) for function optimization

AU - Hagiwara, Masafumi

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0027886606&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027886606&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027886606

SN - 0780314212

SN - 9780780314214

VL - 1

SP - 713

EP - 716

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Publ by IEEE

ER -