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Genetic Algorithms for Multiobjective Optimization: Formulation Discussion and Generalization

Genetic Algorithms for Multiobjective Optimization: Formulation Discussion and Generalization,Carlos M. Fonseca,Peter J. Fleming

Genetic Algorithms for Multiobjective Optimization: Formulation Discussion and Generalization   (Citations: 883)
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Abstract The paper describes a rank-based tness as- signment method for Multiple Objective Ge- netic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The t- ness assignment method is then modied,to allow direct intervention of an external deci- sion maker (DM). Finally, the MOGA is gen- eralised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satis- factory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-o surface.
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