MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning

MOLeCS is a classifier system (CS) which addresses its learning as a multiobjective task. Its aim is to develop an optimal set of rules, optimizing the accuracy and the generality of each rule simultaneously. This is achieved by considering these two goals in the rule fitness. The paper studies four multiobjective strategies that establish a compromise between accuracy and generality in different ways. The results suggest that including the decision maker’s preferences in the search process improves the overall performance of the obtained rule set. The paper also studies a third major objective: covering (the maintenance of a set of different rules solving together the learning problem), through different niching mechanisms. After a performance analysis using some benchmark problems, MOLeCS is applied to a real-world categorization task: the diagnosis of breast cancer.