Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/71718
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Type: Book chapter
Title: Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions
Author: Neumann, F.
O'Reilly, U.
Wagner, M.
Citation: Genetic Programming Theory and Practice IX, 2011 / Riolo, R., Vladislavleva, E., Moore, J. (ed./s), pp.113-128
Publisher: Springer
Publisher Place: United States
Issue Date: 2011
Series/Report no.: Genetic and Evolutionary Computation
ISBN: 9781461417699
Statement of
Responsibility: 
Frank Neumann, Una-May O’Reilly and Markus Wagner
Abstract: The computational complexity analysis of evolutionary algorithmsworking on binary strings has significantly increased the rigorous understanding on how these types of algorithm work. Similar results on the computational complexity of genetic programming would fill an important theoretic gap. They would significantly increase the theoretical understanding on how and why genetic programming algorithms work and indicate, in a rigorous manner, how design choices of algorithm components impact its success. We summarize initial computational complexity results for simple tree-based genetic programming and point out directions for future research.
Keywords: Genetic programming; computational complexity analysis; theory
Description: Genetic and Evolutionary Computation Series
RMID: 0020117915
DOI: 10.1007/978-1-4614-1770-5
Appears in Collections:Computer Science publications

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