Publications (53) View all
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Article: Speeding up the evaluation phase of GP classification algorithms on GPUs
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ABSTRACT: The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820×. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.Soft Computing 01/2012; 16:187-202. · 1.88 Impact Factor -
Article: ReliefF-MI: An extension of ReliefF to Multiple Instance Learning
Neurocomputing. 01/2012; 75(1):210-218. -
Article: Speeding up the evaluation phase of GP classification algorithms on GPUs.
Soft Comput. 01/2012; 16:187-202. -
Conference Proceeding: A Parallel Genetic Programming Algorithm for Classification.
Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part I; 01/2011 -
Article: A Gene Expression Programming Algorithm for Multi-Label Classification.
Multiple-Valued Logic and Soft Computing. 01/2011; 17:183-206.
