Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

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.

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