Abstract
We propose a performance enhancement using parallelization of genetic operations that takes highly fit schemata (building-block) linkages into account. Previously, we used the problem of solving Sudoku puzzles to demonstrate the possibility of shortening processing times through the use of many-core processors for genetic computations. To increase accuracy, we proposed a genetic operation that takes building-block linkages into account. Here, in an evaluation using very difficult problems, we show that the proposed genetic operations are suited to fine-grained parallelization; processing performance increased by approximately 30 % (four times) with fine-grained parallel processing of the proposed mutation and crossover methods on Intel Core i5 (NVIDIA GTX5800) compared with non-parallel processing on a CPU. Increasing GPU resources will diminish the conflicts with thread usage in coarse-grained parallelization of individuals and will enable faster processing.
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Acknowledgments
This research is partly supported by the collaborative research program 2012, Information Initiative Center, Hokkaido University, and a grant from the Institute for Sustainability Research and Education of Hosei University 2012.
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Sato, Y., Inoue, H. & Sato, M. Parallelization of genetic operations that takes building-block linkage into account. Artif Life Robotics 17, 17–23 (2012). https://doi.org/10.1007/s10015-012-0012-x
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DOI: https://doi.org/10.1007/s10015-012-0012-x