Hybrid real-coded genetic algorithms with female and male differentiation

Author

Garcia-Martinez, C. ; Lozano, M.

Author_Institution

Comput. Sci. & AI Dept., Univ. of Granada

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

896

Abstract

Parent-centric real-parameter crossover operators create the offspring in the neighborhood of one of the parents, the female parent, using a probability distribution. The other parent, the male one, defines the range of this probability distribution. The female and male differentiation process determines the individuals in the population that may become female or/and male parents. An important property of this process is that it makes possible the design of two kinds of real-coded genetic algorithms: ones that promote global search and ones that are effective local searchers. In this paper, we study the performance of a hybridization of these real-coded genetic algorithms when tackling the test problems proposed for the Special Session on Real-Parameter Optimization of the IEEE Congress on Evolutionary Computation 2005

Keywords

genetic algorithms; probability; search problems; evolutionary computation; female differentiation; hybrid real-coded genetic algorithm; male differentiation; optimization; parent-centric real-parameter crossover operator; probability distribution; search problem; Algorithm design and analysis; Application software; Biological cells; Computer science; Evolutionary computation; Genetic algorithms; Noise measurement; Probability distribution; Sampling methods; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554778

Filename

1554778