Backward-chaining Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Riccardo Poli and William B. Langdon",
  title =        "Backward-chaining Genetic Programming",
  institution =  "Department of Computer Science, University of Essex",
  year =         "2005",
  type =         "Technical Report",
  number =       "CSM-425",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  ISSN =         "1744-8050",
  abstract =     "Tournament selection is the most frequently used form
                 of selection in genetic programming (GP). Tournament
                 selection chooses individuals uniformly at random from
                 the population. As noted in [7], even if this process
                 is repeated many times in each generation, there is
                 always a nonzero probability that some of the
                 individuals in the population will not be involved in
                 any tournament. In certain conditions, typical in GP,
                 the number of individuals in this category can be
                 large. Because these individuals have no influence on
                 future generations, it is possible to avoid creating
                 and evaluating them without altering in any significant
                 way the course of a run. [7] proposed an algorithm, the
                 backward chaining EA (BC-EA), to realised this, but
                 provided limited empirical evidence of the actual
                 savings and the experiments were restricted to
                 fixed-length genetic algorithms. In contrast we provide
                 a generational genetic programming implementation of
                 BC-EA and empirically investigate the efficiency in
                 terms of fitness evaluations and memory use and
                 effectiveness in terms of ability to solve problems of
                 BC-GP. Results indicate that large savings can be
                 obtained with this approach.",
  notes =        "Poly-10",
  size =         "18 pages",

Genetic Programming entries for Riccardo Poli William B Langdon