Article ID: | iaor20084672 |
Country: | Netherlands |
Volume: | 177 |
Issue: | 3 |
Start Page Number: | 1703 |
End Page Number: | 1719 |
Publication Date: | Mar 2007 |
Journal: | European Journal of Operational Research |
Authors: | Teghem Jacques, Elaoud Samya, Loukil Taicir |
Keywords: | programming: multiple criteria |
Evolutionary algorithms have shown some success in solving multiobjective optimization problems. The methods of fitness assignment are mainly based on the information about the dominance relation between individuals. We propose a Pareto fitness genetic algorithm in which we introduce a modified ranking procedure and a promising way of sharing; a new fitness function based on the rank of the individual and its density value is designed. This is considered as our main contribution. The performance of our algorithm is evaluated on six multiobjective benchmarks with different Pareto front features. Computational results (quality of the approximation of the Pareto optimal set and the number of fitness function evaluations) proving its efficiency are reported.