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Handbook of Grammatical Evolution pp 395–405Cite as

Grammatical Evolution Strategies for Bioinformatics and Systems Genomics

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Abstract

Evolutionary computing methods are an attractive option for modeling complex biological and biomedical systems because they are inherently parallel, they conduct stochastic search through large solution spaces, they capitalize on the modularity of solutions, they have flexible solution representations, they can utilize expert knowledge, they can consider multiple fitness criteria, and they are inspired by how evolution optimizes fitness through natural selection. Grammatical evolution (GE) is a promising example of evolutionary computing because it generates solutions to a problem using a generative grammar. We review here several detailed examples of GE from the bioinformatics and systems genomics literature and end with some ideas about the challenges and opportunities for integrating GE into biological and biomedical discovery.

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Authors and Affiliations

  1. Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Jason H. Moore & Moshe Sipper

  2. Computer Science Department, Ben-Gurion University, Beersheba, Israel

    Moshe Sipper

Authors
  1. Jason H. Moore
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  2. Moshe Sipper
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Correspondence to Jason H. Moore .

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Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    Conor Ryan

  2. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  3. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    JJ Collins

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Moore, J.H., Sipper, M. (2018). Grammatical Evolution Strategies for Bioinformatics and Systems Genomics. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-78717-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78716-9

  • Online ISBN: 978-3-319-78717-6

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