Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of fea...

• Type: Conference
• Year: 2009
• Where: AUSAI
• Authors: Stephanus Daniel Handoko, Chee Keong Kwoh, Yew-Soon Ong
• Keywords: Artificial Intelligence, AUSAI 2009, Feasible Solutions, Function Evaluations, Optimization Problems