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Evolving Cell Array Configurations Using CGP

  • Paul Bremner
  • Mohammad Samie
  • Gabriel Dragffy
  • Anthony. G. Pipe
  • Yang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

A cell array is a proposed type of custom FPGA, where digital circuits can be formed from interconnected configurable cells. In this paper we have presented a means by which CGP might be adapted to evolve configurations of a proposed cell array. As part of doing so, we have suggested an additional genetic operator that exploits modularity by copying sections of the genome within a solution, and investigated its efficacy. Additionally, we have investigated applying selection pressure for parsimony during functional evolution, rather than in a subsequent stage as proposed in other work. Our results show that solutions to benchmark problems can be evolved with a good degree of efficiency, and that compact solutions can be found with no significant impact on the required number of circuit evaluations.

Keywords

Genetic Programming Logic Gate Benchmark Problem Digital Circuit Node Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paul Bremner
    • 1
  • Mohammad Samie
    • 1
  • Gabriel Dragffy
    • 1
  • Anthony. G. Pipe
    • 1
  • Yang Liu
    • 2
  1. 1.Bristol Robotics LaboratoryUniversity of the West of EnglandBristolUK
  2. 2.Intelligent Systems Group, Department of ElectronicsUniversity of YorkHeslington, YorkUK

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