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A Knowledge-Based Approach to Initial Population Generation in Evolutionary Algorithms: Application to the Protein Structure Prediction Problem

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Language, Culture, Computation. Computing - Theory and Technology

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8001))

Abstract

In this study we introduce a new approach to generate the initial population of an Evolutionary Algorithm (EA), based on problem-specific knowledge. We discuss the key ingredients (knowledge and diversity) necessary to generate a good diverse initial random population, with particular application to the protein structure prediction problem. Two main components of our Initial Population Generation (IPG) algorithm are described: (a) one provides the bio-chemical problem-specific knowledge (Molecular Dynamics (MD) and Normal Mode Analysis (NMA)); (b) the second one is an algorithm which ensures population diversity by using the complete graph of the generated bio-molecular conformations. Results show that IPG is a promising algorithm for the creation of good diversity initial populations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Jerusalem College of Technology, Jerusalem, 91160, Israel

    Erick Fredj

  2. Department of Environmental Science, Weizmann Institute, Rehovot, 76100, Israel

    Erick Fredj & Moshe Goldstein

  3. Department of Physical Chemistry and The Fritz Haber Research Center, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel

    Moshe Goldstein

Authors
  1. Erick Fredj
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  2. Moshe Goldstein
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Editor information

Editors and Affiliations

  1. School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Nachum Dershowitz

  2. Department of Computing, University of London, Goldsmiths College, 25–27 St. James, New Cross, SE14 6NW, London, UK

    Ephraim Nissan

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Fredj, E., Goldstein, M. (2014). A Knowledge-Based Approach to Initial Population Generation in Evolutionary Algorithms: Application to the Protein Structure Prediction Problem. In: Dershowitz, N., Nissan, E. (eds) Language, Culture, Computation. Computing - Theory and Technology. Lecture Notes in Computer Science, vol 8001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45321-2_13

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  • DOI: https://doi.org/10.1007/978-3-642-45321-2_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45320-5

  • Online ISBN: 978-3-642-45321-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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