Abstract
This chapter introduces the main principles of evolutionary computation (EC) and presents a methodology for using it to optimize the parameters and the set of features (e.g. genes, brain signals) in a computational model. Evolutionary computation (EC) methods adopt principles from the evolution in Nature (Darwin 1859). EC methods are used in Chaps. 7 and 8 of the book to optimize gene interaction networks as part of a CNGM.
Keywords
- Genetic Algorithm
- Evolutionary Computation
- Travel Salesman Problem
- Lifelong Learning
- Feature Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2007 Springer Science + Business Media, LLC
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Benuskova, L., Kasabov, N. (2007). Evolutionary Computation for Model and Feature Optimization. In: Computational Neurogenetic Modeling. Topics in Biomedical Engineering. International Book Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-48355-9_6
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DOI: https://doi.org/10.1007/978-0-387-48355-9_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-48353-5
Online ISBN: 978-0-387-48355-9
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