An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profits to a company under resource constraints. In this chapter, we first formulate this learning problem as a constrained optimization problem and then convert it to an unconstrained multi-objective optimization problem (MOP), which can be handled by some multi-objective evolutionary algorithms (MOEAs). However, MOEAs may execute for a long time for theMOP, because several evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. Thus we propose a parallel MOEA on consumer-level graphics processing units (GPU) to tackle the MOP. We perform experiments on a real-life direct marketing problem to compare the proposed method with the parallel hybrid genetic algorithm, the DMAX approach, and a sequential MOEA. It is observed that the proposed method is much more effective and efficient than the other approaches.
WONG, M. L., & CUI, G. (2013). Data mining using parallel multi-objective evolutionary algorithms on graphics processing units. In Massively Parallel Evolutionary Computation on GPGPUs (pp. 287-307). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-642-37959-8_14