Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in many-objective optimization. However, most of existing research within this area focused on improving the environmental selection procedure, and …
Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the …
In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimization that allows to work with different types of fitness functions and ensemble ranking schemes. The framework aims to rank the …