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Evolving Solutions: The Genetic Algorithm and Evolution Strategies for Finding Optimal Parameters

  • Chapter
Applications of Computational Intelligence in Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 122))

Summary

This chapter provides an introduction to evolutionary algorithms (EAs) and their applicability to various biological problems. There is a focus on EAs’ use as an optimization technique for fitting parameters to a model. A number of design issues are discussed including the data structure being operated on (chromosome), the construction of robust fitness functions, and intuitive breeding strategies. Two detailed biological examples are given. The first example demonstrates the EA’s ability to optimize parameters of various ion channel conductances in a model neuron by using a fitness function that incorporates the dynamic range of the data. The second example shows how the EA can be used in a hybrid technique with classification algorithms for more accuracy in the classifier, feature pruning, and for obtaining relevant combinations of features. This hybrid technique allows researchers to glean an understanding of important features and relationships embedded in their data that might otherwise remain hidden.

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McTavish, T., Restrepo, D. (2008). Evolving Solutions: The Genetic Algorithm and Evolution Strategies for Finding Optimal Parameters. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Applications of Computational Intelligence in Biology. Studies in Computational Intelligence, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_3

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  • DOI: https://doi.org/10.1007/978-3-540-78534-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78533-0

  • Online ISBN: 978-3-540-78534-7

  • eBook Packages: EngineeringEngineering (R0)

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