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The paper reviews several genetic algorithm (GA) approaches to multi objective optimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto optimal solutions are generated. From this viewpoint, the paper reviews several devices proposed for multi objective optimization by GAs such as the parallel selection method, the Pareto based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.
Tamaki et al. (Mon,) studied this question.