Multiobjective genetic algorithms

Multiobjective genetic algorithms (MOGAs) are introduced as a modification of the standard genetic algorithm at the selection level. Rank-based fitness assignment and the implementation of sharing in the objective value domain are two of the important aspects of this class of algorithms. The ability of the decision maker (DM) to progressively articulate its preferences while learning about the problem under consideration is one of their most attractive features. 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-off surface.<
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