Search Operator Bias in Linearly Structured Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Garnett C. Wilson and Malcolm I. Heywood",
  title =        "Search Operator Bias in Linearly Structured Genetic
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "GA solutions to the job-shop scheduling problem
                 demonstrate that significant amounts of code context
                 exist. Such observations have led to the introduction
                 of biased search operators. In this work, we recognise
                 that similar conditions exist in linearly structured GP
                 (L-GP). An empirical study is made when biased search
                 operators are applied to the San Mateo Trail
                 (strategy), Two Box (regression), and Liver Disease
                 (classification) benchmark problems. A preference is
                 observed for biased mutation alone in the case of the
                 regression problem, whereas the strategy and
                 classification problems appear to prefer the
                 combination of both biased mutation and crossover.",
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",

Genetic Programming entries for Garnett Carl Wilson Malcolm Heywood