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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
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)