Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonabeau, E.W., Dorigo, M., Theraulaz, G.: Swarm Intelligence: from natural to artificial systems. Addison Wesley, Reading (1999)
Brits, R., Engelbrecht, A.P., van den Bergh, F.: A niching Particle Swarm Optimizer. In: 4th Asia-Pacific Conference on Simulated Evolution and Learning (2002)
Brown, S., Rose, J.: Architecture of FPGAs and CPLDs: a tutorial. IEEE Design and Test of Computers 13(2), 42–57 (1996)
Celoxica. Handel-C Reference Manual, development kit v3.0 (2004), http://www.celoxica.com/techlib/files/CEL-W0410251JJ4-60.pdf
Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: a characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)
de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Garrett, S.M.: Parameter-free, adaptive clonal selection. In: CEC 2004, pp. 1052–1058. IEEE Press, Los Alamitos (2004)
Hoare, C.A.R.: Communicating Sequential Processes. Prentice Hall, Englewood Cliffs (1985)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Kim, J., Bentley, P.J.: Immune memory in the dynamic clonal selection algorithm. In: ICARIS 2002, Kent, pp. 59–67 (2002)
Mahfoud, S.W.: A comparison of parallel and sequential niching methods. In: Eshelman, L.J. (ed.) Proc. 6th International Conference on Genetic Algorithms, pp. 136–143. Morgan Kaufmann, San Francisco (1995)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Stepney, S.: CSP/FDR2 to Handel-C translation. Technical Report YCS-2003-357, University of York (June 2003)
Stepney, S., Smith, R.E., Timmis, J., Tyrrell, A.M., Neal, M.J., Hone, A.N.W.: Conceptual Frameworks for Artificial Immune Systems. Int. J. Unconventional Computing 1(3) (2005)
Watkins, A., Timmis, J.: Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 427–438. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Newborough, J., Stepney, S. (2005). A Generic Framework for Population-Based Algorithms, Implemented on Multiple FPGAs. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_4
Download citation
DOI: https://doi.org/10.1007/11536444_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
Online ISBN: 978-3-540-31875-0
eBook Packages: Computer ScienceComputer Science (R0)