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Differential Evolution Algorithm: Foundations and Perspectives

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Book cover Metaheuristic Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 178))

Abstract

Differential Evolution (DE) has recently emerged as simple and efficient algorithm for global optimization over continuous spaces.DE shares many features of the classical Genetic Algorithms (GA). But it is much easier to implement than GA and applies a kind of differential mutation operator on parent chromosomes to generate the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world, resulting in a lot of variants of the basic algorithm, with improved performance. This chapter begins with a conceptual outline of classical DE and then presents several significant variants of the algorithm in greater details.

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Authors
  1. Swagatam Das
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  2. Ajith Abraham
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  3. Amit Konar
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Das, S., Abraham, A., Konar, A. (2009). Differential Evolution Algorithm: Foundations and Perspectives . In: Metaheuristic Clustering. Studies in Computational Intelligence, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93964-1_2

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  • DOI: https://doi.org/10.1007/978-3-540-93964-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92172-1

  • Online ISBN: 978-3-540-93964-1

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