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

• Published in: Artificial Immune Systems pp. 43 - 55
• Main Authors: Newborough, John, Stepney, Susan
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial Immune System; Artificial intelligence; Clonal Selection Algorithm; Computer science; control theory; systems; Exact sciences and technology; Field Programmable Gate Array; Main Population; Prototype Implementation; Multiagent system; Biomimetics; Genetic algorithm; Evolutionary algorithm; Parallelism; Population dynamics; Swarm intelligence; Field programmable gate array; Neural network; Artificial intelligence; Immune system
• ISBN: 3540281754; 9783540281757
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11536444_4

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.