Function Stacks, GBEAs, and Crossover for the Parity Problem

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  author =       "Daniel Ashlock and Kenneth M. Bryden",
  title =        "Function Stacks, GBEAs, and Crossover for the Parity
  booktitle =    "ANNIE 2006, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2006",
  editor =       "Cihan H. Dagli and Anna L. Buczak and 
                 David L. Enke and Mark Embrechts and Okan Ersoy",
  volume =       "16",
  address =      "St. Louis, MO, USA",
  month =        nov # " 5-8",
  note =         "Part I: Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "0791802566",
  DOI =          "doi:10.1115/1.802566.paper18",
  abstract =     "Function stacks are a directed acyclic graph
                 representation for genetic programming that subsumes
                 the need for automatically defined functions,
                 substantially reduces the number of operations required
                 to solve a problem, and permits the use of a
                 conservative crossover operator. Function stacks are a
                 generalisation of Cartesian genetic programming. Graph
                 based evolutionary algorithms are a method for
                 improving evolutionary algorithm performance by
                 imposing a connection topology on an evolutionary
                 population to strike an efficient balance between
                 exploration and exploration. In this study the parity
                 problems using function stacks for parity on 3, 4, 5,
                 and 6 variables are tested on fifteen graphical
                 connection topologies with and without crossover.
                 Choosing the correct graph is found to have a
                 statistically significant impact on time to solution.
                 The conservative crossover operator for function
                 stacks, new in this study, is found to improve time to
                 solution by 4 to 9 fold with more improvement in harder
                 instances of the parity problem.",

Genetic Programming entries for Daniel Ashlock Kenneth M Bryden