A Generic Framework for Population-Based Algorithms, Implemented on Multiple FPGAs

  • John Newborough
  • Susan Stepney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3627)


Many bio-inspired algorithms (evolutionary algorithms, artificial immune systems, particle swarm optimisation, ant colony optimisation,...) are based on populations of agents. Stepney et al [2005] argue for the use of conceptual frameworks and meta-frameworks to capture the principles and commonalities underlying these, and other bio-inspired algorithms. Here we outline a generic framework that captures a collection of population-based algorithms, allowing commonalities to be factored out, and properties previously thought particular to one class of algorithms to be applied uniformly across all the algorithms. We then describe a prototype proof-of-concept implementation of this framework on a small grid of FPGA (field programmable gate array) chips, thus demonstrating a generic architecture for both parallelism (on a single chip) and distribution (across the grid of chips) of the algorithms.


Field Programmable Gate Array Artificial Immune System Prototype Implementation Main Population Clonal Selection Algorithm 
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 2005

Authors and Affiliations

  • John Newborough
    • 1
  • Susan Stepney
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkHeslington, YorkUK

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