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Advancing genetic algorithm approaches to field programmable gate array placement with enhanced recombination operators

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Abstract

Since their inception, field programmable gate arrays have seen an enormous growth in usage because they can dramatically reduce design and manufacturing costs. However, the time required for placement (a key step in the design) is dominating the compilation process. In this paper, we take some initial theoretical steps towards developing an efficient genetic algorithm for solving the placement problem by developing suitable recombination operators for performing placement. According to Holland, when the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Those recombination operators that violate this principle can direct a search away from the region containing the parent genotypes and this is contrary to the intended task for recombination. This is often detrimental to search performance. This paper contributes the development of an intuitive visualization technique that can be used to easily detect violations of the previous principle. The efficacy of the proposed methodology is demonstrated and it is demonstrated that many standard recombination operators violate this principle. The methodology is then used to guide the development of novel operators that exhibit substantial (and statistically significant) improvements in performance over standard recombination operators.

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Acknowledgments

The material is based on work supported by the natural sciences and engineering research council of Canada.

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Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Robert Collier

  2. Faculty of Engineering and Computer Science, University of Guelph, Guelph, ON, Canada

    Christian Fobel, Ryan Pattison, Gary Grewal & Shawki Areibi

  3. Department of Electrical and Computer Engineering, Miami University, Oxford, OH, USA

    Peter Jamieson

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  1. Robert Collier
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  2. Christian Fobel
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  3. Ryan Pattison
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  4. Gary Grewal
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  5. Shawki Areibi
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  6. Peter Jamieson
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Correspondence to Robert Collier.

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Collier, R., Fobel, C., Pattison, R. et al. Advancing genetic algorithm approaches to field programmable gate array placement with enhanced recombination operators. Evol. Intel. 7, 183–200 (2014). https://doi.org/10.1007/s12065-014-0114-6

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  • DOI: https://doi.org/10.1007/s12065-014-0114-6

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