Multi-objective classification based on NSGA-II

Abstract

The fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II) is currently the most popular multi-objective evolutionary algorithm (MOEA). NSGA-II has been shown to work well for two-objective problems by attaining near-optimal diverse and uniformly distributed Pareto solutions. To use the powerful multi-objective optimisation performance of NSGA-II directly and conveniently, an optimisation classification model is presented. In the optimisation classification model, a linear equation set is constructed according to classification problems. In this paper, we introduced NSGA-II to solve the optimisation classification model. Besides, eight different datasets have been chosen in experiments to test the performance of NSGA-II. The results show that NSGA-II is able to find much better spread of solutions and has high classification accuracy and robustness.