Abstract
Genetic Algorithms are heuristic search approaches that are applicable to a wide range of optimization problems. This flexibility makes them attractive for many optimization problems in practice. Evolution is the basis of Genetic Algorithms. The current variety and success of species is a good reason for believing in the power of evolution. Species are able to adapt to their environment. They have developed to complex structures that allow the survival in different kinds of environments. Mating and getting offspring to evolve belong to the main principles of the success of evolution. These are good reasons for adapting evolutionary principles to solving optimization problems.
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Kramer, O. (2017). Genetic Algorithms. In: Genetic Algorithm Essentials. Studies in Computational Intelligence, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-52156-5_2
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DOI: https://doi.org/10.1007/978-3-319-52156-5_2
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