Robust Optimization by epsilon-Ranking on High Dimensional Objective Spaces

Abstract

This work proposes a method to fine grain the ranking of solutions after they 
have been ranked by Pareto dominance, aiming to improve the performance of 
evolutionary algorithms oil many objective's optimization problems. The re-ranking 
method uses a randomized sampling procedure to choose, from sets of equally 
ranked solutions, those solutions that, will be given selective advantage,. The 
sampling procedure favors a good distribution of the, sampled solutions based 
on dominance regions wider than conventional Pareto dominance. We enhance, 
NSGA-II with the proposed method and test its performance on with up to M = 10 
objectives. Experimental result's show that, convergence and diversity of the 
solutions found call improve remarkably oil 3 <= M <= 10 objectives problems.