Many bio-inspired algorithms (evolutionary algorithms, artificial immune systems, particle swarm optimisation, ant colony optimisation, ) are based on populations of agents. Stepney et al  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.
@inproceedings(SS-ICARIS-05, author = "John Newborough and Susan Stepney", title = "A generic framework for population-based algorithms, implemented on multiple FPGAs", pages = "43--55", crossref = "ICARIS-05" ) @proceedings(ICARIS-05, title = "ICARIS 2005: Fourth International Conference on Artificial Immune Systems, Banff, Canada, August 2005", booktitle = "ICARIS 2005: Fourth International Conference on Artificial Immune Systems, Banff, Canada, August 2005", series = "LNCS", volume = 3627, publisher = "Springer", year = 2005 )