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A Population Diversity-Oriented Gene Expression Programming for Function Finding

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Simulated Evolution and Learning (SEAL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6457))

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Abstract

Gene expression programming (GEP) is a novel evolutionary algorithm, which combines the advantages of simple genetic algorithm (SGA) and genetic programming (GP). Owing to its special structure of linear encoding and nonlinear decoding, GEP has been applied in various fields such as function finding and data classification. In this paper, we propose a modified GEP (Mod-GEP), in which, two strategies including population updating and population pruning are used to increase the diversity of population. Mod-GEP is applied into two practical function finding problems, the results show that Mod-GEP can get a more satisfactory solution than that of GP, GEP and GEP based on statistical analysis and stagnancy (AMACGEP).

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Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071

    Ruochen Liu, Qifeng Lei, Jing Liu & Licheng Jiao

Authors
  1. Ruochen Liu
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  2. Qifeng Lei
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  3. Jing Liu
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  4. Licheng Jiao
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  2. Department of Computer Science andl Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  3. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, 712302, Kharagpur, West Bengal, India

    Nirupam Chakraborti

  4. Department of Civil Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Partha Chakroborty  & Ashu Jain  & 

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

    Swagatam Das

  6. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Joydeep Dutta

  7. Department of Chemical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Santosh K. Gupta

  8. Aspiring Minds, 24 Pusa Road, 110005, New Delhi, India

    Varun Aggarwal

  9. Warwick Business School, University of Warwick, CV4 7AL, Coventry, UK

    Jürgen Branke

  10. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    Sushil J. Louis

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

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© 2010 Springer-Verlag Berlin Heidelberg

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Liu, R., Lei, Q., Liu, J., Jiao, L. (2010). A Population Diversity-Oriented Gene Expression Programming for Function Finding. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_22

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  • DOI: https://doi.org/10.1007/978-3-642-17298-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17297-7

  • Online ISBN: 978-3-642-17298-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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