Data mining using parallel multi-objective evolutionary algorithms on graphics processing units

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Abstract

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.
Original languageEnglish
Title of host publicationMassively Parallel Evolutionary Computation on GPGPUs
PublisherSpringer-Verlag GmbH and Co. KG
Pages287-307
Number of pages21
ISBN (Print)9783642379581
DOIs
Publication statusPublished - 1 Jan 2013

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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