Rake Selection: A Novel Evolutionary Multi-Objective Optimization Algorithm

Abstract

The optimization of multiple conflictive objectives at the same time is a hard problem. In most cases, a uniform distribution of solutions on the Pareto front is the main objective. We propose a novel evolutionary multi-objective algorithm that is based on the selection with regard to equidistant lines in the objective space. The so-called rakes can be computed efficiently in high dimensional objective spaces and guide the evolutionary search among the set of Pareto optimal solutions. First experimental results reveal that the new approach delivers a good approximation of the Pareto front with uniformly distributed solutions. As the algorithm is based on a (μ + λ)-Evolution Strategy with birth surplus it can use σ-self-adaptation. Furthermore, the approach yields deeper insights into the number of solutions that are necessary for a uniform distribution of solutions in high-dimensional objective spaces.