Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/70655
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Type: Conference paper
Title: Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics
Author: Durrett, G.
Neumann, F.
O'Reilly, U.
Citation: Proceeding: FOGA '11: Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, 2011: pp.69-80
Publisher: ACM Press
Publisher Place: New York
Issue Date: 2011
ISBN: 9781450306331
Conference Name: ACM SIGEVO Workshop on Foundations of Genetic Algorithms (11th : 2011 : Schwarzenberg, Austria)
Statement of
Responsibility: 
Greg Durrett, Frank Neumann, Una-May O’Reilly
Abstract: Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyze them on two separable model problems, ORDER and MAJORITY, each of which captures a relevant facet of typical GP problems. Both analyses give first rigorous insights into aspects of GP design, highlighting in particular the impact of accepting or rejecting neutral moves and the importance of a local mutation operator.
Keywords: Algorithms; Theory
Rights: Copyright 2011 ACM
RMID: 0020110566
DOI: 10.1145/1967654.1967661
Description (link): http://www.sigevo.org/foga-2011/
Appears in Collections:Computer Science publications

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