Data mining using parallel multi-objective evolutionary algorithms on graphics processing units
Document Type
Book chapter
Source Publication
Massively Parallel Evolutionary Computation on GPGPUs
Publication Date
1-1-2013
First Page
287
Last Page
307
Publisher
Springer Verlag
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.
DOI
10.1007/978-3-642-37959-8_14
Funding Information
This work is supported by Hong Kong RGC General Research Fund LU310111. {LU310111}
Publisher Statement
Copyright © Springer-Verlag Berlin Heidelberg 2013. Access to external full text or publisher's version may require subscription.
Additional Information
ISBN of the source publication: 9783642379581
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Wong, M. L., & Cui, G. (2013). Data mining using parallel multi-objective evolutionary algorithms on graphics processing units. In Tsutsui S. & Collet P. (Eds.), Massively Parallel Evolutionary Computation on GPGPUs (pp.287-307). Berlin: Springer. doi: 10.1007/978-3-642-37959-8_14