TY - GEN
T1 - Pseudo-hill climbing genetic algorithm (PHGA) for function optimization
AU - Hagiwara, Masafumi
PY - 1993/12/1
Y1 - 1993/12/1
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
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M3 - Conference contribution
AN - SCOPUS:0027886606
SN - 0780314212
SN - 9780780314214
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 713
EP - 716
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Publ by IEEE
T2 - Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
Y2 - 25 October 1993 through 29 October 1993
ER -