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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Misc{Durrett:2010:ccaGP2pmips,
  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",
  year =         "2010",
  month =        "27 " # jul,
  note =         "arXiv:1007.4636v1",
  keywords =     "genetic algorithms, genetic programming, Computational
                 Complexity, Data Structures and Algorithms",
  URL =          "http://arxiv.org/pdf/1007.4636v1",
  size =         "26 pages",
  abstract =     "Analysing the computational complexity of evolutionary
                 algorithms for binary search spaces has significantly
                 increased their theoretical understanding. With this
                 paper, we start the computational complexity analysis
                 of genetic programming. We set up several simplified
                 genetic programming algorithms and analyze them on two
                 separable model problems, ORDER and MAJORITY, each of
                 which captures an important facet of typical genetic
                 programming problems. Both analyses give first rigorous
                 insights on aspects of genetic programming design,
                 highlighting in particular the impact of accepting or
                 rejecting neutral moves and the importance of a local
                 mutation operator.",
  notes =        "See \cite{Durrett:2011:foga}",
}

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

Citations