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-objective optimization problems. Our approach differs from single-objective genetic algorithms in its selection procedure and elite presence strategy. The selection procedure in our genetic algorithms selects individuals for a crossover operation based on a weighted sum of multiple objective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple objective functions are not constant but randomly specified for each selection. The elite preserve strategy in our genetic algorithms uses multiple elite solutions instead of a single elite 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