Abstract
Evolutionary algorithms (EAs) are a set of optimization and machine learning techniques that find their inspiration in the biological processes of evolution established by Darwin [27] and other scientists in the ninenteenth century. Starting from a population of individuals that represent admissible solutions to a given problem through a suitable coding, these metaheuristics leverage the principles of variation by mutation, and recombination, and of selection of the best-performing individuals in a given environment. By iterating this process the system finds increasingly good solutions and generally solves the problem satisfactorily.
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Chopard, B., Tomassini, M. (2018). Evolutionary Algorithms: Foundations. In: An Introduction to Metaheuristics for Optimization. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-319-93073-2_8
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DOI: https://doi.org/10.1007/978-3-319-93073-2_8
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