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Theory of Evolutionary Algorithms and Genetic Programming

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Advances in Computational Intelligence

Part of the book series: Natural Computing Series ((NCS))

Summary

Randomized search heuristics are an alternative to specialized and problem-specific algorithms. They are applied to NP-hard problems with the hope of being efficient in typical cases. They are an alternative if no problem-specific algorithm is available. And they are the only choice in black-box optimization where the function to be optimized is not known. Evolutionary algorithms (EA) are a special class of randomized algorithms with many successful applications. However, the theory of evolutionary algorithms is in its infancy. Here many new contributions to constructing such a theory are presented and discussed.

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Authors and Affiliations

  1. Department of Computer Science, Informatik II, University of Dortmund, 44221, Dortmund, Germany

    Stefan Droste, Thomas Jansen, Karsten Tinnefeld & Ingo Wegener

  2. Department of Computer Science, Informatik XI, University of Dortmund, 44221, Dortmund, Germany

    Günter Rudolph & Hans-Paul Schwefel

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  1. Stefan Droste
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  2. Thomas Jansen
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  3. Günter Rudolph
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  4. Hans-Paul Schwefel
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  5. Karsten Tinnefeld
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  6. Ingo Wegener
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Editors and Affiliations

  1. Fachbereich Informatik Lehrstuhl Informatik XI, Universität Dortmund, 44221, Dortmund, Germany

    Hans-Paul Schwefel

  2. Lehrstuhl Informatik II, Universität Dortmund, 44221, Dortmund, Germany

    Ingo Wegener

  3. Fakultät Maschinenbau Institut für Spannende Fertigung, Universität Dortmund, 44221, Dortmund, Germany

    Klaus Weinert

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Droste, S., Jansen, T., Rudolph, G., Schwefel, HP., Tinnefeld, K., Wegener, I. (2003). Theory of Evolutionary Algorithms and Genetic Programming. In: Schwefel, HP., Wegener, I., Weinert, K. (eds) Advances in Computational Intelligence. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05609-7_5

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