Abstract--Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to fo...
One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two c...
In previous work, a neural network was used to increase the number of solutions found by an evolutionary multiobjective optimization algorithm. In this paper, various approaches a...
Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision s...
In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach ...
We focus on the handling of overlapping solutions in evolutionary multiobjective optimization (EMO) algorithms. First we show that there exist a large number of overlapping soluti...
Abstract. Evolutionary algorithms have been quite effective in dealing with single-objective “optimization” while the area of Evolutionary Multiobjective Optimization (EMOO) h...
Abstract. The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea t...
In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover...
A new evolutionary technique for multicriteria optimization called Guiding Hyper-plane Evolutionary Algorithm (GHEA) is proposed. The originality of the approach consists in the f...