Two new GA-based methods for multiobjective optimization

In this paper, we introduce two new multiobjective optimization techniques based on the genetic algorithm (GA), and implemented as part of a multiobje ctive optimization tool called MOSES (Multiobjective Optimization of System s in the Engineering Sciences). These methods are based in the concept of m in-max optimum, and can produce the Pareto set and the best trade-off among the objectives. The results produced by these approaches are compared to t hose produced with other mathematical programming techniques and GA-based a pproaches using two engineering design problems, showing the new techniques ' capability to generate better trade-offs than the approaches previously r eported in the literature.