MOGA: multi-objective genetic algorithms

In this paper, we propose a .framework of genetic algorithms to search for Pareto optimal solutions (i.e., non-dominated solutions) of multi-ohjectiv,e optimizution problems. Our approuch d!fers from single-objective genetic algorithms in its selection proceduiae and elite preserve strategy. The selection procedure in our genetic algorithms selects individuals for a cromover operation based on a weighted sum of multiple ohjective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple objective ,functions are not constant but rundomly specified for each selection. 7he elite preserve strategy in our genetic algorithms uses multiple elite solutions instead of a single eliie solution. That is, a certain number of individuals are selected from a tentative set of Pareto optimal solutions and inherited to the next generation as elite individuals.

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