The research of parameters of genetic algorithm and comparison with particle swarm optimization and shuffled frog-leaping algorithm

Author

Yue, Mei ; Tao Hu ; Hu, Tao ; Guo, Xuan

Author_Institution

Inst. of Inf. Eng., Shenzhen Univ., Shenzhen, China

Volume

1

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

77

Lastpage

80

Abstract

The paper introduces the principle of genetic algorithm and analyses the selection of parameters of genetic algorithm. By an example, the paper researches the different effect of each parameter. Such as, the size of the population (M), the probability of crossover (P_c) and the probability of mutation (P_m). By the experimentation and simulation, The paper brings forward a general method for selection of parameters for genetic algorithm. In the end, the paper compare the genetic algorithm (GA) with particle swarm optimization (PSO) and shuffled frog-leaping algorithm (SFLA).

Keywords

genetic algorithms; particle swarm optimisation; genetic algorithm; parameter selection; particle swarm optimization; shuffled frog-leaping algorithm; Biological cells; Evolution (biology); Genetic algorithms; Genetic engineering; Genetic mutations; Intelligent transportation systems; Paper technology; Particle swarm optimization; Power engineering and energy; Probability; crossover; genetic algorithm; mutation; particle swarm optimization; shuffled frog-leaping algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-4544-8

Type

conf

DOI

10.1109/PEITS.2009.5406960

Filename

5406960