Multi-objective Rule Discovery Using the Improved Niched Pareto Genetic Algorithm
Junli Lu, Fan Yang, Momo Li, Lizhen Wang · 2011
We present an efficient genetic algorithm for mining multi-objective rules from large databases. Multi-objectives will conflict with each other, which makes it optimization problem that is very difficult to solve simultaneously. We propose a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm(INPGA), which not only accurate selects the candidates but also saves selection time with combining BNPGA and SDNPGA. Because the effect of selection operator relies on the samples, we proposed clustering-based sampling method, and we also consider the situation of zero niche count. We have compared the execution time and rules generation by INPGA with that by BNPGA and SDNPGA. The experimental results confirm that our method has edge over BNPGA and SDNPGA.