Abstract
This book gives an introduction to concepts and ideas of Genetic Algorithms. Before it begins, it is reasonable to clarify, what kinds of problems are solved with Genetic Algorithms. The answer is simple and short: optimization problems. Optimization is the task of finding optimal solutions, which are solutions that have a better quality than others . We often seek for the global optimal solution, which is the best solution in the whole solution space. This can be a tedious task, as the solution space can suffer from constraints, noise, strange fitness function conditions, unsteadiness, and a large number of local optima. If modeled in an appropriate kind of way, Genetic Algorithms are able to solve most optimization problems that occur in practice.
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Kramer, O. (2017). Introduction. In: Genetic Algorithm Essentials. Studies in Computational Intelligence, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-52156-5_1
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DOI: https://doi.org/10.1007/978-3-319-52156-5_1
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