Hierarchical genetic programming based on test input subsets

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

@InProceedings{1277280,
  author =       "David Jackson",
  title =        "Hierarchical genetic programming based on test input
                 subsets",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1612--1619",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1612.pdf",
  DOI =          "doi:10.1145/1276958.1277280",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming,
                 decomposition, hierarchical GP, program architecture",
  abstract =     "Crucial to the more widespread use of evolutionary
                 computation techniques is the ability to scale up to
                 handle complex problems. In the field of genetic
                 programming, a number of decomposition and reuse
                 techniques have been devised to address this. As an
                 alternative to the more commonly employed encapsulation
                 methods, we propose an approach based on the division
                 of test input cases into subsets, each dealt with by an
                 independently evolved code segment. Two program
                 architectures are suggested for this hierarchical
                 approach, and experimentation demonstrates that they
                 offer substantial performance improvements over more
                 established methods. Difficult problems such as even-10
                 parity are readily solved with small population
                 sizes.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for David Jackson

Citations