Evolutionary multi-objective optimization deals with the task of computing a minimal set of search points according to a given set of objective functions. The task has been made e...
Rudolf Berghammer, Tobias Friedrich, Frank Neumann
In Multi-Objective Problems (MOPs) involving uncertainty, each solution might be associated with a cluster of performances in the objective space depending on the possible scenari...
: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Alth...
Abstract. This paper presents a novel perspective to the use of multiobjective optimization and in particular evolutionary multi-objective optimization (EMO) as a measure of comple...
In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research field, there has been few trials to adapt the general variation operators to t...
—This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to synthesize multiple complex modules on programmable devices (FPGAs). It starts ...
Evolutionary multi-objective optimization of spiking neural networks for solving classification problems is studied in this paper. By means of a Paretobased multi-objective geneti...
— Solution diversity in evolutionary multi-objective optimization is considered. Although the Pareto front is ubiquitously used for the multi-objective optimization, the method o...
— A cognitive system is presented, which is based on coupling a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to ide...