Data mining using parallel multi-objective evolutionary algorithms on graphics hardware

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6 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
PublisherIEEE
ISBN (Electronic)9781424469116, 9781424469109
ISBN (Print)9781424469093
DOIs
Publication statusPublished - Jul 2010
Event2010 IEEE Congress on Evolutionary Computation (CEC) - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Conference

Conference2010 IEEE Congress on Evolutionary Computation (CEC)
Period18/07/1023/07/10

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Bibliographical note

Paper presented at the 2010 IEEE World Congress on Computational Intelligence, Jul 18-23, 2010, Barcelona, Spain.

Cite this

WONG, M. L., & CUI, G. (2010). Data mining using parallel multi-objective evolutionary algorithms on graphics hardware. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 IEEE. https://doi.org/10.1109/CEC.2010.5586161