Data mining using parallel multi-objective evolutionary algorithms on graphics hardware
Document Type
Conference paper
Source Publication
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Publication Date
1-1-2010
Abstract
An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first formulate this learning problem as a constrained optimization problem and then converse it to an unconstrained Multi-objective Optimization Problem (MOP). A parallel Multi-Objective Evolutionary Algorithm (MOEA) on consumer-level graphics hardware is used to handle 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
10.1109/CEC.2010.5586161
ISBN
9781424469109
Publisher Statement
Copyright © 2010 IEEE. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 2010 IEEE World Congress on Computational Intelligence, Jul 18-23, 2010, Barcelona, Spain.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Wong, M.-L., & Cui, G. (2010). Data mining using parallel multi-objective evolutionary algorithms on graphics hardware. In IEEE Congress on Evolutionary Computation, Barcelona, 2010 (pp.1-8). doi: 10.1109/CEC.2010.5586161