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

John Newborough, Susan Stepney, Christian Jacob, Marcin L. Pilat, Peter J. Bentley, Jonathan Timmis

Research output: Contribution to conferencePaper


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.
Original languageUndefined/Unknown
Publication statusPublished - 2005

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