Improving the performance and scalability of differential evolution

Iorio, A and Li, X 2008, 'Improving the performance and scalability of differential evolution', in X. Li et al (ed.) The 7th International Conference on Simulated Evolution and Learning, Proceedings, Melbourne, Australia, 7-10 December 2008, pp. 131-140.


Document type: Conference Paper
Collection: Conference Papers

Title Improving the performance and scalability of differential evolution
Author(s) Iorio, A
Li, X
Year 2008
Conference name The 7th International Conference on Simulated Evolution and Learning , SEAL 2008
Conference location Melbourne, Australia
Conference dates 7-10 December 2008
Proceedings title The 7th International Conference on Simulated Evolution and Learning, Proceedings
Editor(s) X. Li et al
Publisher Springer
Place of publication Berlin, Germany
Start page 131
End page 140
Total pages 10
Abstract Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity. The new DE approach demonstrates fast convergence towards the global optimum and is highly scalable in the decision space.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1007/978-3-540-89694-4_14
Copyright notice © Springer-Verlag Berlin Heidelberg 2008
ISSN 0302-9743
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