Adaptive local search parameters for real-coded memetic algorithms

Author

Molina, Daniel ; Herrera, Francisco ; Lozano, Manuel

Author_Institution

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

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

888

Abstract

This paper presents a real-coded memetic algorithm that combines a high diversity global exploration with an adaptive local search method to the most promising individuals that adjusts the local search probability and the local search depth. In our proposal we use the individual fitness to decide when local search will be applied (local search probability) and how many effort should be applied (the local search depth), focusing the local search effort on the most promising regions. We divide the individuals of the population into three different categories and we assign different values of the above local search parameters to the individual in function of the category to which that individual belongs. In this study, we analyze the performance of our proposal when tackling the test problems proposed for the Special Session of the IEEE Congress on Evolutionary Computation 2005

Keywords

genetic algorithms; probability; search problems; adaptive local search parameters; evolutionary computation; high diversity global exploration; local search depth; local search probability; real-coded memetic algorithm; Algorithm design and analysis; Biological cells; Computer science; Evolutionary computation; Genetic algorithms; Genetic mutations; Performance analysis; Proposals; Search 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.2005.1554777

Filename

1554777