Multi-objective GA rule extraction in a parallel framework

Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. Multiobjective evolutionary algorithms (MOEAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) O(MN3) or O(MN2) computational complexity (where M is the number of objectives and N is the population size); 2) nonelitism approach [?]; and 3) the need for specifying a sharing parameter. In this paper, a new simple yet efficient approach is proposed to improve the performance of Multi-objective GA-based classifiers; the computational complexity of the proposed technique is O(MN), we also used a class decomposition technique. A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. We experimentally evaluate our approach on three different datasets and demonstrate that our algorithm can improve classification rate compared with normal GA and nonpartioned techniques; our technique is optimized using OpenMP-like implementation to take advantage of multi-threads or multi-processors.