Document Clustering Using Multi-Objective Genetic Algorithms on MATLAB Distributed Computing
Jung Song Lee, Soon Cheol Park · 2012
Genetic Algorithm (GA), one of the artificial intelligence algorithms, performs much better than the other algorithms for the document clustering. However, it has problem known as the premature convergence occurrence. So, Fuzzy Logic based GA (FLGA) was proposed to solve it. Nevertheless, it has still weakness such as the parameter dependence problem. In order to overcome this problem, the Multi-Objective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, have been proposed. The MOGAs showed high performance compared to other algorithms including the general GA, but their computational times have increased. In order to reduce these computational times, the distributed computing method, MATLAB Distributed Computing (MDC) with 10 processors, is applied to the MOGAs in this paper. The performances of MOGAs on MDC show about 12% higher than others.