Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Durrett:2011:foga,

• author = "Greg Durrett and Frank Neumann and Una-May O'Reilly",
• title = "Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics",
• booktitle = "Foundations of Genetic Algorithms",
• year = "2011",
• editor = "Hans-Georg Beyer and W. B. Langdon",
• pages = "69--80",
• address = "Schwarzenberg, Austria",
• month = "5-9 " # jan,
• organisation = "SigEvo",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Genetic Programming Theory, Computational Complexity, Hill Climbing",
• isbn13 = "978-1-4503-0633-1",
• DOI = "doi:10.1145/1967654.1967661",
• size = "12 pages",
• abstract = "Analysing 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 analyse 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.",
• notes = "See \cite{Durrett:2010:ccaGP2pmips} \cite{Nguyen:2013:foga} FOGA11 ACM order number 910114",

}

Genetic Programming entries for Greg Durrett Frank Neumann Una-May O'Reilly

Citations