Migrational GA that preserves solutions in non-static optimization problems

Author

Hartono, Pitoyo ; Hashimoto, Shuji

Author_Institution

Adv. Res. Inst. for Sci. & Eng., Waseda Univ., Tokyo, Japan

Volume

1

fYear

2001

fDate

2001

Firstpage

255

Abstract

The genetic algorithm (GA) has been successfully introduced to solve various optimization problems. One of the characteristics of the GA is that, once it has converged, most of its population members are copies of the best individual, causing the GA to lose population diversity. This characteristic is a setback when we consider non-stationary problems in which the fitness functions vary with time. In this paper, we propose a migrational GA that stores past environmental solutions and retrieves them rapidly when that environment is re-activated, through probabilistic operation

Keywords

genetic algorithms; probability; convergence; environment reactivation; environmental change; individual copies; migrational genetic algorithm; nonstatic optimization problems; nonstationary problems; past environmental solution retrieval; population diversity; population members; probabilistic operation; solution preservation; sub-populations; varying fitness functions; Biological cells; Genetic mutations; Physics; Timing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2001 IEEE International Conference on

Conference_Location

Tucson, AZ

ISSN

1062-922X

Print_ISBN

0-7803-7087-2

Type

conf

DOI

10.1109/ICSMC.2001.969821

Filename

969821