An Adaptive Penalty Scheme for Steady-State Genetic Algorithms

Helio J.C. Barbosa¹ and Afonso C.C. Lemonge²

¹LNCC/MCT
Rua Getulio Vargas 333
25651 070 Petropolis RJ, BRAZIL
hcbm@lncc.br

²Depto. de Estruturas,
Faculdade de Engenharia
Universidade Federal de Juiz de Fora
36036 330 Juiz de Fora MG, BRAZIL
lemonge@numec.ufjf.br

Abstract. A parameter-less adaptive penalty scheme for steady-state genetic algorithms applied to constrained optimization problems is proposed. For each constraint, a penalty parameter is adaptively computed along the run according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. Using real coding, rank-based selection, and operators available in the literature, very good results are obtained.

LNCS 2723, p. 718 ff.

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