Departamento de Computación

In this presentation we address the convergence of generic stochastic search algorithms (such as EMO algorithms) toward the Pareto front of continuous multi-objective optimization problems. The focus is on obtaining a finite size and gap-free approximation that should capture the entire solution set in a suitable sense, which will be defined using the concept of ε-dominance. Under mild assumptions about the process to generate new candidate solutions, the limit approximation set will be determined entirely by the archiving strategy. We investigate in this talk such a strategy theoretically (convergence, upper bound of the archive) as well as with respect to its practical relevance. (See also http://paradiseo.gforge.inria.fr/)