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Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units

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Massively Parallel Evolutionary Computation on GPGPUs

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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. 1.

    Cache for global memory is only available in GPUs of compute capability 2.x.

  2. 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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Acknowledgements

This work is supported by Hong Kong RGC General Research Fund LU310111.

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Correspondence to Man Leung Wong .

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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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  • DOI: https://doi.org/10.1007/978-3-642-37959-8_14

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