Abstract
The choice of the most appropriate model is a critical component of any interpretation of ecological data. That choice is most often made arbitrarily and the choice is usually heavily weighted by the familiarity of the analyst with the selected model. Genetic algorithms, which represent a class of algorithms designed to find the best solution to a given problem by using ideas from biology, especially genetics, provide an alternative approach to model selection. Beginning with a brief description of how genetic algorithms work, two different algorithms are used to select appropriate models for typical case studies.
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© 1999 Springer Science+Business Media New York
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Jeffers, J.N.R. (1999). Genetic Algorithms I. In: Fielding, A.H. (eds) Machine Learning Methods for Ecological Applications. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5289-5_4
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DOI: https://doi.org/10.1007/978-1-4615-5289-5_4
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7413-8
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