Stochastic Diversity Loss and Scalability in Estimation of Distribution Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Kim:2012:ieeeTEC,

• author = "Kangil Kim and Bob (R. I.) Mckay",
• title = "Stochastic Diversity Loss and Scalability in Estimation of Distribution Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2013",
• volume = "17",
• number = "3",
• pages = "301--320",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithm (EDA), Evolutionary Computation (EC), Genetic Programming (GP), Likelihood Weighting (LW), Probabilistic Prototype Tree (PPT), diversity loss, sampling bias, sampling drift",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2012.2196521",
• size = "20 pages",
• abstract = "In Estimation of Distribution Algorithms (EDA), probability models hold accumulating evidence on the location of an optimum. Stochastic sampling drift has been heavily researched in EDA optimisation, but not in EDAs applied to Genetic Programming (EDA-GP). We show that, for EDA-GPs using Probabilistic Prototype Tree (PPT) models, stochastic drift in sampling and selection is a serious problem, inhibiting scaling to complex problems. Problems requiring deep dependence in their probability structure see such rapid stochastic drift that the usual methods for controlling drift are unable to compensate. We propose a new alternative, analogous to likelihood weighting of evidence. We demonstrate in a small-scale experiment that it does counteract the drift, sufficiently to leave EDA-GP systems subject to similar levels of stochastic drift to other EDAs.",
• notes = "Max problem \cite{langdon:1997:MAX} and onemax, 3 bit even parity. Sampling drift, stochastic drift, premature convergence. undefined allele U. 'The sample size is reduced by the number of individuals sampled as U.' 'The performance of both EDA systems is substantially worse...' '2) Some probability tables give zero probability to the correct alleles' 'They won't help EDA-GP to scale to the problem complexities typically handelled by today's GP systems...' imputing missing values. porr performance on near trivial problems DCTG-GP \cite{ross:2001:ngc} also known as \cite{6189777}",

}

Genetic Programming entries for Kangil Kim R I (Bob) McKay

Citations