Institute of Electrical & Electronic Engineers
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, the authors 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. They 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.