Computational Science Technical Note CSTN-192

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Geometric Optimisation using Karva for Graphical Processing Units

Alwyn V. Husselmann and K. A. Hawick

Archived: 2013

Abstract

Population-based evolutionary algorithms continue to play an important role in artifically intelligent systems, but can not always easily use parallel computation. We have combined a geometric (any-space) particle swarm optimisation algorithm with use of Ferreira's Karva language of gene expression programming to produce a hybrid that can accelerate the genetic operators and which can rapidly attain a good solution. We show how Graphical Processing Units (GPUs) can be exploited for this. While the geometric particle swarm optimiser is not markedly faster that genetic programming, we show it does attain good solutions faster, which is important for the problems discussed when the fitness function is inordinately expensive to compute.

Keywords: CUDA; geometric; genetic programming; gpu; parallel; particle swarm

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-192,
        author = {Alwyn V. Husselmann and K. A. Hawick},
        title = {Geometric Optimisation using Karva for Graphical Processing Units},
        booktitle = {Proc. 15th International Conference on Artificial Intelligence (ICAI'13)},
        year = {2013},
        number = {CSTN-191},
        pages = {ICA2335},
        address = {Las Vegas, USA},
        month = {22-25 July},
        publisher = {WorldComp},
        institution = {Computer Science, Massey University, Auckland, New Zealand},
        keywords = {CUDA; geometric; genetic programming; gpu; parallel; particle swarm},
        owner = {kahawick},
        timestamp = {2013.03.19}
}


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