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 the MOP, 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.
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Notes
- 1.
Cache for global memory is only available in GPUs of compute capability 2.x.
- 2.
In CUDA, the GPU creates, manages, schedules, and executes threads in groups of 32 parallel threads called warps. A half-warp is either the first or second half of a warp.
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This work is supported by Hong Kong RGC General Research Fund LU310111.
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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. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_14
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