Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

Gerulf K.M. Pedersen¹ and David E. Goldberg²

¹Aalborg University, Department of Control Engineering, Fredrik Bajers Vej 7, DK-9220 Aalborg Ø, Denmark

²University of Illinois at Urbana-Champaign, Department of General Engineering, 104 S. Mathews Ave., Urbana IL 61801, USA

Abstract. Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can be obtained for badly scaled objective functions. This is especially a problem if the bounds for the objective functions are unknown, which may result in the non-dominated solutions found by the MOEA to be biased towards one objective, thus resulting in a less diverse set of tradeoffs. In this paper, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and the proposed solutions will be implemented using the NSGA-II algorithm.

LNCS 3103, p. 11 ff.

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