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Differential Evolution

  • Kenneth V. Price
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)

Abstract

After an introduction that includes a discussion of the classic random walk, this paper presents a step-by-step development of the differential evolution (DE) global numerical optimization algorithm. Five fundamental DE strategies, each more complex than the last, are evaluated based on their conformance to invariance and symmetry principles, degree of control parameter dependence, computational efficiency and response to randomization. Optimal control parameter settings for the family of convex, quadratic functions are empirically derived.

Keywords

Random Walk Differential Evolution Success Performance Differential Evolution Algorithm Target Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Pacific BellVacavilleU.S.A.

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