Please use this identifier to cite or link to this item:
https://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 |
Editor: | Riolo, R. Vladislavleva, E. Moore, J. |
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 |
DOI: | 10.1007/978-1-4614-1770-5 |
Published version: | http://dx.doi.org/10.1007/978-1-4614-1770-5 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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RA_hdl_71718.pdf Restricted Access | Restricted Access | 294.73 kB | Adobe PDF | View/Open |
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