Constraint-handling using an evolutionary multiobjective optimization technique

In this paper, we introduce the concept of non-dominance (commonly used in multiobjective optimization) as a way to incorporate constraints into the f itness function of a genetic algorithm. Each individual is assigned a rank based on its degree of dominance over the rest of the population. Feasible individuals are always ranked higher than infeasible ones, and the degree o f constraint violation determines the rank among infeasible individuals. Th e proposed technique does not require fine tuning of factors like the tradi tional penalty function and uses a self-adaptation mechanism that avoids th e traditional empirical adjustment of the main genetic operators (i.e., cro ssover and mutation).