Speeding up the evaluation of evolutionary learning systems using GPGPUs

In this paper we introduce a method for computing fitness in evolutionary learning systems based on NVIDIA’s massive parallel technology using the CUDA library. Both the match process of a population of classifiers against a training set and the computation of the fitness of each classifier from its matches have been parallelized. This method has been integrated within the BioHEL evolutionary learning system. The methodology presented in this paper can be easily extended to any evolutionary learning system. The method has been tested using a broad set of problems with varying number of attributes and instances. The evaluation function by itself achieves speedups up to 52.4X while its integration with the entire learning process achieves speedups up to 58.1X. Moreover, the speedup increases when the CUDAbased fitness computation is combined with other efficiency enhancement mechanisms. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—Concept Learn

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: María A. Franco, Natalio Krasnogor, Jaume Bacardit
• Keywords: BioHEL Evolutionary Learning, Evolutionary Learning System, GECCO 2010, NVIDIA’s Massive Parallel, Optimization