TY - JOUR
T1 - Performance evaluation and population reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA)
AU - Espinoza, Felipe P.
AU - Minsker, Barbara S.
AU - Goldberg, David E.
PY - 2003
Y1 - 2003
N2 - This paper examines the effects of local search on hybrid genetic algorithm performance and population sizing. It compares the performance of a self-adaptive hybrid genetic algorithm (SAHGA) to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on eight different test functions, including unimodal, multimodal and constrained optimization problems. The results show that the hybrid genetic algorithm substantially reduces required population sizes because of the reduction in population variance. The adaptive nature of the SAHGA algorithm together with the reduction in population size allow for faster solution of the test problems without sacrificing solution quality.
AB - This paper examines the effects of local search on hybrid genetic algorithm performance and population sizing. It compares the performance of a self-adaptive hybrid genetic algorithm (SAHGA) to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on eight different test functions, including unimodal, multimodal and constrained optimization problems. The results show that the hybrid genetic algorithm substantially reduces required population sizes because of the reduction in population variance. The adaptive nature of the SAHGA algorithm together with the reduction in population size allow for faster solution of the test problems without sacrificing solution quality.
UR - http://www.scopus.com/inward/record.url?scp=21144431624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=21144431624&partnerID=8YFLogxK
U2 - 10.1007/3-540-45105-6_104
DO - 10.1007/3-540-45105-6_104
M3 - Article
AN - SCOPUS:21144431624
SN - 0302-9743
VL - 2723
SP - 922
EP - 933
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -