Learning to Control the Program Evolution Process with Cultural Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Elena Zannoni and Robert G. Reynolds",
  title =        "Learning to Control the Program Evolution Process with
                 Cultural Algorithms",
  journal =      "Evolutionary Computation",
  year =         "1997",
  volume =       "5",
  number =       "2",
  pages =        "181--211",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming, cultural
                 algorithms, software design methodologies, software
                 metrics, machine learning of software design concepts,
                 design concept reuse",
  ISSN =         "1063-6560",
  URL =          "http://www.mitpressjournals.org/doi/pdf/10.1162/evco.1997.5.2.181",
  DOI =          "doi:10.1162/evco.1997.5.2.181",
  size =         "31 pages",
  abstract =     "Traditional software engineering dictates the use of
                 modular and structured programming and top-down
                 stepwise refinement techniques that reduce the amount
                 of variability arising in the development process by
                 establishing standard procedures to be followed while
                 writing software. This focusing leads to reduced
                 variability in the resulting products, due to the use
                 of standardized constructs. Genetic programming (GP)
                 performs heuristic search in the space of programs.
                 Programs produced through the GP paradigm emerge as the
                 result of simulated evolution and are built through a
                 bottom-up process, incrementally augmenting their
                 functionality until a satisfactory level of performance
                 is reached. Can we automatically extract knowledge from
                 the GP programming process that can be useful to focus
                 the search and reduce product variability, thus leading
                 to a more effective use of the available resources? An
                 answer to this question is investigated with the aid of
                 cultural algorithms. A new system has two levels. The
                 first is the pool of genetic programs (population
                 level), and the second is a knowledge repository
                 (belief set) that is built during the GP run and is
                 used to guide the search process. The microevolution
                 within the population brings about potentially
                 meaningful characteristics of the programs for the
                 achievement of the given task, such as properties
                 exhibited by the best performers in the population.
                 CAGP extracts these features and represents them as the
                 set of the current beliefs. Beliefs correspond to
                 constraints that all the genetic operators and programs
                 must follow. Interaction between the two levels occurs
                 in one direction through the extraction process and, in
                 the other, through the modulation of an individual's
                 program parameters according to which, and how many, of
                 the constraints it follows. CAGP is applied to solve an
                 instance of the symbolic regression problem, in which a
                 function of one variable needs to be discovered. The
                 results of the experiments show an overall improvement
                 on the average performance of CAGP over GP alone and a
                 significant reduction of the complexity of the produced
                 solution. Moreover, the execution time required by CAGP
                 is comparable with the time required by GP alone.",
  notes =        "Evolutionary Computation (Journal)

                 Special Issue: Trends in Evolutionary Methods for
                 Program Induction PMID: 10021758

                 Cited by \cite{ostrowski:1998:ismcaoalsesd}",

Genetic Programming entries for Elena Zannoni Robert G Reynolds