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Genetic Programming using the Karva Gene Expression Language on Graphical Processing Units

Alwyn V. Husselmann, K. A. Hawick
Computer Science, Massey University, North Shore 102-904, Auckland, New Zealand
Massey University, Computational Science Technical Note CSTN-171, 2013

@inproceedings{CSTN-171,

   author={Alwyn V. Husselmann and K. A. Hawick},

   title={Genetic Programming using the Karva Gene Expression Language on Graphical Processing Units},

   booktitle={Proc. 10th International Conference on Genetic and Evolutionary Methods (GEM’13)},

   year={2013},

   number={CSTN-171},

   pages={GEM2456},

   month={22-25 July},

   publisher={WorldComp},

   institution={Computer Science, Massey University, Auckland, New Zealand},

   keywords={karva language; CUDA; genetic programming; gpu; parallel; optimisation},

   owner={kahawick},

   timestamp={2013.03.19}

}

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Genetic Programming (GP) has been employed in many problem domains, and as a result, it has been the subject of much scientific inquiry. The extensive literature body of GP has reported applications in algorithm discovery, image enhancement and cooperative multi-agent systems, as well as many other areas and disciplines, such as agent-based modelling in Geography and Social Science. As models become more complex, further research toward higher efficiency have been warranted. We discuss solutions to large-scale systems which require automatic programming, and present results of a modified data-parallel implementation of GP based on Gene-expression Programming for Graphical Processing Units (GPUs), as well as a modified Santa Fe Ant Trail problem to measure the efficacy of this algorithm. We present results on algorithm convergence as well as timing performance on both GPU and CPU implementations.
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