Abstract
The Differential Evolution algorithm goes back to the class of Evolutionary Algorithms and inherits its philosophy and concept. Possessing only three control parameters (size of population, differentiation and recombination constants) Differential Evolution has promising characteristics of robustness and convergence. In this paper we introduce a new principle of Energetic Selection. It consists in both decreasing the population size and the computation efforts according to an energetic barrier function which depends on the number of generation. The value of this function acts as an energetic filter, through which can pass only individuals with lower fitness. Furthermore, this approach allows us to initialize the population of a sufficient (large) size. This method leads us to an improvement of algorithm convergence.
This is a preview of subscription content, log in via an institution to check access.
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
Beasley, D. (1997). Possible applications of evolutionary computation. In Bäack, T., Fogel, D. B., and Michalewicz, Z., editors, Handbook of Evolutionary Computation, pages A1.2:1–10. IOP Publishing Ltd. and Oxford University Press, Bristol, New York.
Feoktistov, V. and Janaqi, S. (2004a). Generalization of the strategies in differential evolutions. In 18th Annual IEEE International Parallel and Distributed Processing Symposium. IPDPS — NIDISC 2004 workshop, page (accepted), Santa Fe, New Mexico — USA. IEEE Computer Society.
Feoktistov, V. and Janaqi, S. (2004b). Hybridization of differential evolution with least-square support vector machines. In Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands. BENELEARN 2004., pages 53–57, Vrije Universiteit Brussels, Belgium.
Feoktistov, V. and Janaqi, S. (2004c). New strategies in differential evolution. In Parmee, I., editor, 6-th International Conference on Adaptive Computing in Design and Manufacture, ACDM 2004, page (accepted), Bristol, UK. Engineers House, Clifton, Springer-Verlag Ltd.(London).
Heitkötter, J. and Beasley, D. (2000). Hitch Hiker’s Guide to Evolutionary Computation: A List of Frequently Asked Questions (FAQ).
Jong, K. A. D. (1975). An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan.
Price, K. (2003). New Ideas in Optimization, Part 2: Differential Evolution. McGraw-Hill, London, UK.
Salomon, R. (1996). Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some theoretical and practical aspects of genetic algorithms. BioSystems, 39:263–278.
Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA.
Storn, R. and Price, K. (1996). Minimizing the real functions of the ICEC’96 contest by differential evolution. In IEEE International Conference on Evolutionary Computation, pages 842–844, Nagoya. IEEE, New York, NY, USA.
Whitley, D., Rana, S. B., Dzubera, J., and Mathias, K. E. (1996). Evaluating evolutionary algorithms. Artificial Intelligence, 85(1–2):245–276.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Feoktistov, V., Janaqi, S. (2006). New Energetic Selection Principle in Differential Evolution. In: Seruca, I., Cordeiro, J., Hammoudi, S., Filipe, J. (eds) Enterprise Information Systems VI. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3675-2_18
Download citation
DOI: https://doi.org/10.1007/1-4020-3675-2_18
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3674-3
Online ISBN: 978-1-4020-3675-0
eBook Packages: Computer ScienceComputer Science (R0)