Multi-objective bayesian optimization algorithm
Nazan Khan, David E. Goldberg, Martin Pelikán · 2002
Recently, signicant development in the theory and de-sign of competent genetic algorithms (GAs) has been achieved. By competent GA we mean genetic algo-rithms that can solve boundedly diÆcult problems quickly, accurately, and reliably. However, most of the existing competent GAs focus only on single-objective optimization although many real-world problems con-tain more than one objective. Independently of the de-velopment of competent genetic algorithms, a number of approaches to solve such multiobjective problems have been proposed. However, there has been little or no eort to develop competent multiobjective opera-tors that eÆciently identify, propagate, and combine important partial solutions of the problem at hand. This study makes an eort towards multiobjective