Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms

Author

Kita, Hajime ; Ono, Isao ; Kobayashi, Shigenobu

Author_Institution

Interdisciplinary Graduate Sch. of Sci. & Eng., Tokyo Inst. of Technol., Yokohama, Japan

Volume

2

fYear

1999

fDate

1999

Abstract

The unimodal normal distribution crossover (UNDX) for the real-coded genetic algorithms (RCGA) proposed by Ono et al. (1997, 1998) shows an excellent performance in optimization problems of multi-modal and highly epistatic fitness functions in continuous search space. Further, theoretical analysis of the UNDX shows that the UNDX is a crossover operator that preserves the statistics such as the mean vector and the covariance matrix of the population well. The present paper proposes some design guidelines for crossover operators for RCGA. Then, based on these guidelines, a multi-parental extension of the UNDX is proposed so as to enhance its exploration ability. Performance of the extended UNDX is evaluated by numerical experiments

Keywords

genetic algorithms; normal distribution; numerical analysis; continuous search space; covariance matrix; crossover operator; exploration ability; highly epistatic fitness functions; mean vector; multi-modal fitness functions; multi-parental extension; numerical experiments; optimization problems; performance; population well; real-coded genetic algorithms; statistics; unimodal normal distribution crossover; Covariance matrix; Gaussian distribution; Genetic algorithms; Guidelines; Lenses; Optical design; Sampling methods; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.782672

Filename

782672