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

Man Leung WONG; Geng CUI

doi:10.1109/CEC.2010.5586161

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

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

8 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 language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Publisher	IEEE
ISBN (Electronic)	9781424469116, 9781424469109
ISBN (Print)	9781424469093
DOIs	https://doi.org/10.1109/CEC.2010.5586161
Publication status	Published - Jul 2010
Event	2010 IEEE Congress on Evolutionary Computation (CEC) - Barcelona, Spain Duration: 18 Jul 2010 → 23 Jul 2010

Conference

Conference	2010 IEEE Congress on Evolutionary Computation (CEC)
Period	18/07/10 → 23/07/10

Bibliographical note

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

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10.1109/CEC.2010.5586161

Cite this

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title = "Data mining using parallel multi-objective evolutionary algorithms on graphics hardware",

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.",

author = "WONG, {Man Leung} and Geng CUI",

note = "Paper presented at the 2010 IEEE World Congress on Computational Intelligence, Jul 18-23, 2010, Barcelona, Spain.; 2010 IEEE Congress on Evolutionary Computation (CEC) ; Conference date: 18-07-2010 Through 23-07-2010",

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doi = "10.1109/CEC.2010.5586161",

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Data mining using parallel multi-objective evolutionary algorithms on graphics hardware. / WONG, Man Leung ; CUI, Geng.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. IEEE, 2010.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AB - 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.

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BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

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T2 - 2010 IEEE Congress on Evolutionary Computation (CEC)

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

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