Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

Gerulf Pedersen, D.E. Goldberg

Research output: Working paperResearch

7 Citations (Scopus)
263 Downloads (Pure)


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 problems 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, this 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 proposed solutions will be implemented using the NSGA-II algorithm.
Original languageDanish
Publication statusPublished - 2004

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