NDLI: Genetic algorithm with dual species

Content Provider	IEEE Xplore Digital Library
Author	Li Junhua Li Ming Li Junhua
Copyright Year	2008
Description	Author affiliation: Key Lab. of Nondestructive Test, Nanchang HangKong Univ., Nanchang (Li Junhua; Li Ming)
Abstract	In this paper, a new genetic algorithm with two species is proposed. Our dual species genetic algorithm (DSGA) composes of two subpopulation that constitute of same size individuals. The subpopulations have different characteristics, such as crossover probability and mutation operator. In one subpopulation, the parents with higher similarity are cross with higher rate; mutate with general mutation operator. So that, the new algorithm can obtains good exploitation ability. In the other subpopulation, the parents with smaller similarity are cross with higher rate; mutate with big mutation rate. So that, the new algorithm can gets good exploration ability. The performance of our DSGA is compare to that of a single population genetic algorithm (SPGA) and Multi-population genetic algorithm with two populations (2PMGA). The experimental results show that the proposed method can gain higher global convergence rate and higher speed.
Starting Page	2572
Ending Page	2575
File Size	525345
Page Count	4
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636604
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gallium adaptive crossover probability Genetics Robustness multi-population genetic algorithms Optimization Biological cells Genetic algorithms Convergence
Content Type	Text
Resource Type	Article

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