Abstract
The idea of mimicking processes of organic evolution on computers and using such algorithms for solving adaptation and optimization tasks can be traced back to the Sixties. Genetic Algorithms (GA), Evolutionary Programming (EP), and Evolution Strategies (ES), the still vivid different strata of this idea, have not only survived until now, but have become an important tool within what has been called Computational Intelligence, Soft Computing, as well as Natural Computation. An outline of Evolutionary Algorithms (EA - the common denominator for GA, EP, and ES) will be sketched, their differences pinpointed, some theoretical results summarized, and some applications mentioned.
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© 1999 Springer-Verlag Berlin Heidelberg
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Schwefel, HP. (1999). Natural Computation. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_1
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DOI: https://doi.org/10.1007/3-540-48873-1_1
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