Grammatical Evolution Strategies for Bioinformatics and Systems Genomics



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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Biomedical InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaUSA
  2. 2.Computer Science DepartmentBen-Gurion UniversityBeershebaIsrael

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