Geração genética multiobjetivo de bases de conhecimento fuzzy com enfoque na distribuição das soluções não dominadas

Abstract

The process of building the knowledge base of fuzzy systems has benefited extensively of methods to automatically extract the necessary knowledge from data sets that represent examples of the problem. Among the topics investigated in the most recent research is the matter of balance between accuracy and interpretability, which has been addressed by means of multi-objective genetiv algorithms, NSGA-II being on of the most popular. In this scope, we identified the need to control the diversity of solutions found by these algorithms, so that each solution would balance the Pareto frontier with respect to the goals optimized by the multi-objective genetic algorithm. In this PhD thesis a multi-objective genetic algorithm, named NSGA-DO, is proposed. It is able to find non dominated solutions that balance the Pareto frontier with respect optimization of the objectives. The main characteristicof NSGA-DO is the distance oriented selection of solutions. Once the Pareto frontier is found, the algorithm uses the locations of the solutions in the frontier to find the best distribution of solutions. As for the validation of the proposal, NSGA-DO was applied to a methodology for the generation of fuzzy knowledge bases. Experiments show the superiority of NSGADO when compared to NSGA-II in all three issues analyzed: dispersion, accuracy and interpretability.