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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2010: Swarm, Evolutionary, and Memetic Computing pp 71–78Cite as

Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

Abstract

Differential Evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. Thus, to obtain optimal performance, time consuming parameter tuning is necessary. Different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper, we propose a DE with an ensemble of mutation and crossover strategies and their associated control parameters known as EPSDE. In EPSDE, a pool of distinct mutation and crossover strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of 25 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with state-of-the-art algorithm.

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Author information

Authors and Affiliations

  1. Nanyang Technological university, Singapore, 639798

    Rammohan Mallipeddi & Ponnuthurai Nagaratnam Suganthan

Authors
  1. Rammohan Mallipeddi
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  2. Ponnuthurai Nagaratnam Suganthan
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

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Mallipeddi, R., Suganthan, P.N. (2010). Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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