Multi-objective optimization in evolutionary algorithms using satisfiability classes

Many optimization problems consist of several mutually dependent subproblems, where the resulting solutions must satisfy all requirements. We propose a new model for Multi-Objective Optimization (MOO) in Evolutionary Algorithms (EAs). The search space is partitioned into so-called Satisfiability Classes (SC), where each region represents the quality of the optimization criteria. Applying the SCs to individuals in a population a fitness can be assigned during the EA run. The model also allows the, handling of infeasible regions and restrictions in the search space. Additionally, different priorities for optimization objectives can be modeled. Advantages of the model over previous approaches are discussed and an application is given that shows the superiority of the method for modeling MOO problems.