A coevolutionary genetic algorithm for constrained optimization

Author

Barbosa, Helio J C

Author_Institution

LNCC/CNPq, Petroplis, Brazil

Volume

3

fYear

1999

fDate

1999

Abstract

A co-evolutionary genetic algorithm is proposed for solving constrained optimization problems written as a min-max problem after the introduction of an augmented Lagrangian functional. Two populations are evolved, using in each one, an independent GA. The GA running in population A(B) is a minimization (maximization) one and the individuals in this population encode values of the variable x(y) belonging to the corresponding set X(Y). The GA evolves for a certain number of generations on population A while the other population is kept “frozen”. Then the process is applied to population B and the cycle is repeated. The fitness computation is based on the Lagrangian and the fitness of each individual in one population depends on all individuals of the other population. The results of some numerical experiments are presented

Keywords

constraint theory; genetic algorithms; minimax techniques; minimisation; set theory; augmented Lagrangian functional; co-evolutionary genetic algorithm; coevolutionary genetic algorithm; constrained optimization; fitness computation; independent GA; min-max problem; numerical experiments; population A; population B; Constraint optimization; Decoding; Evolutionary computation; Genetic algorithms; Lagrangian functions; Lead; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.785466

Filename

785466