An Adaptive Penalty Scheme for Steady-State Genetic Algorithms

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.

• Type: Conference
• Year: 2003
• Where: GECCO
• Authors: Helio J. C. Barbosa, Afonso C. C. Lemonge
• Keywords: Adaptive Penalty Scheme, Constrained Optimization Problems, GECCO 2003, Steady-state Genetic Algorithms