Finding a diverse set of decision variables in evolutionary many-objective optimization

Authors:
Kaname Narukawa
Affiliations:
Honda Research Institute Europe GmbH, Offenbach am Main, Germany
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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Abstract

In this paper, we modify an evolutionary many-objective optimization algorithm so that it can find a diverse set of solutions in the decision variable space. The modification is based on considering the Euclidean distance in the decision variable space. The effect of our modification is examined by using benchmark test problems. From computational experiments, we can say that a diverse set of solutions in the decision variable space is searched by the modification.