Evolutionary multiobjective optimization using a cultural algorithm

In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an algorithm representative of the state-of-the-art in evolutionary multiobjective optimization. The performance of our approach indicates that cultural algorithms are a viable alternative for multiobjective optimization.