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Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 11))

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Abstract

In the first chapter we saw that Evolutionary Computation use the concepts of natural evolution to efficiently search the solution of an optimization problem.

In the previous chapter we described the basic concepts of Evolutionary Computation. We discussed the natural underpinnings of evolution that were used as motivation and guidelines for EC. Then we described its main paradigms: Genetic Algorithms,Genetic Programming, Evolution Strategies and Evolutionary Programming. Finally, we introduced the essential components of an Evolutionary Computing method, showing how to put them together in a simple evolutionary optimization system.

However, Evolutionary Computing as a field already exists for more than 30 years. In this period, many issues in the basic methods were identified, and solutions for those issues were developed. These solutions have developed into full subsystems of the evolutionary method themselves, each worthy of a whole volume for detailed explanations.

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Correspondence to Hitoshi Iba .

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© 2012 Springer Berlin Heidelberg

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Iba, H., Aranha, C.C. (2012). Advanced Topics in Evolutionary Computation. In: Practical Applications of Evolutionary Computation to Financial Engineering. Adaptation, Learning, and Optimization, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27648-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-27648-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27647-7

  • Online ISBN: 978-3-642-27648-4

  • eBook Packages: EngineeringEngineering (R0)

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