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Evolutionary Programming Improved by an Individual Random Difference Mutation

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

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

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Abstract

Evolutionary programming (EP) is a classical evolutionary algorithm for continuous optimization. There have been several EP algorithms proposed based on different mutations strategies like Gaussian, Cauchy, Levy and other stochastic distributions. However, their convergence speed should be improved. An EP based on individual random difference (EP-IRD) was proposed to attain better solutions in a higher speed. The mutation of EP-IRD uses a random difference of individuals selected randomly to update the variance with which offspring are generated. The IRD-based mutation can make the better offspring according to the current population faster than the mathematical stochastic distribution. The numerical results of solving benchmark problems indicate that EP-IRD performs better than other four EP algorithms based on mathematical stochastic distribution in the items of convergence speed, optimal value on average and standard deviation.

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Author information

Authors and Affiliations

  1. Education Technology Center, Huizhou University, Huizhou, 516007, P.R. China

    Zhaoquan Cai

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, P.R. China

    Han Huang & Xueqiang Li

  3. State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, 210093, P.R. China

    Han Huang

  4. Dept of Management Sciences, College of Business, City University of Hong Kong, Hong Kong, China

    Han Huang

  5. Faculty of Computer Science, Guangdong University of Technology, 510006, Guangzhou, P.R. China

    Zhifeng Hao

Authors
  1. Zhaoquan Cai
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  2. Han Huang
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  3. Zhifeng Hao
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  4. Xueqiang Li
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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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© 2010 Springer-Verlag Berlin Heidelberg

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Cai, Z., Huang, H., Hao, Z., Li, X. (2010). Evolutionary Programming Improved by an Individual Random Difference Mutation. 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_41

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_41

  • 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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