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Towards Grammatical Evolution-Based Automated Design of Differential Evolution Algorithm

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Congress on Intelligent Systems (CIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1335))

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Abstract

Differential evolution (DE) is a robust evolutionary algorithm that has been applied for various real world optimization problems. However, the performance of DE depends on the optimal choice of variation operators and control parameters. The dizzying choice of heuristics for choosing mutation strategies, crossover operator and control parameters makes DE design a challenging task for a practitioner. We present a meta-evolutionary approach with grammatical evolution (GE) to evolve effective parameter configurations for classical differential evolution algorithm. It has been observed that the GE evolved DE configurations performed competitively on the chosen ten standard benchmark functions. This work is a preliminary step towards automating DE algorithm designs, which has the potential to relieve a user from the painful task of trial-and-error manual designs.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    M. T. Indu & C. Shunmuga Velayutham

Authors
  1. M. T. Indu
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  2. C. Shunmuga Velayutham
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Corresponding author

Correspondence to M. T. Indu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  3. National Institute of Technology, Jalandhar, Punjab, India

    Anupam Yadav

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Indu, M.T., Shunmuga Velayutham, C. (2021). Towards Grammatical Evolution-Based Automated Design of Differential Evolution Algorithm. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_27

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