Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{ramos-criado:ECJ,
-
author = "Pablo {Ramos Criado} and D. {Barrios Rolania} and
David {de la Hoz} and Daniel Manrique",
-
title = "Estimation of Distribution Algorithm for
Grammar-Guided Genetic Programming",
-
journal = "Evolutionary Computation",
-
note = "Online Early",
-
keywords = "genetic algorithms, genetic programming,
Grammar-guided genetic programming, SEDA, EDA,
estimation of distribution algorithms, genetic
variation operators, local search, locality,
search-space exploration",
-
ISSN = "1063-6560",
-
URL = "https://oa.upm.es/82783/3/Manuscrito_Evolutionary_Computation.pdf",
-
DOI = "doi:10.1162/evco_a_00345",
-
size = "30 pages",
-
abstract = "Genetic variation operators in grammar-guided genetic
programming are fundamental to guide the evolutionary
process in search and optimization problems. However,
they show some limitations,mainly derived from an
unbalanced exploration and local-search trade-off. This
article presents an estimation of distribution
algorithm for grammar-guided genetic programming to
overcome this difficulty and thus increase the
performance of the evolutionary algorithm. Our proposal
employs an extended dynamic stochastic context-free
grammar to encode and calculate the estimation of the
distribution of the search space from some promising
individuals in the population. Unlike traditional
estimation of distribution algorithms, the proposed
approach improves exploratory behavior by smoothing the
estimated distribution model. Therefore, this algorithm
is referred to as SEDA, smoothed estimation of
distribution algorithm. Experiments have been conducted
to compare overall performance using a typical genetic
programming crossover operator, an incremental
estimation of distribution algorithm, and the proposed
approach after tuning their hyperparameters. These
experiments involve challenging problems to test the
local search and exploration features of the three
evolutionary systems. The results show that
grammar-guided genetic programming with SEDA achieves
the most accurate solutions with an intermediate
convergence speed.",
-
notes = "A turing Research, Salamanca, Spain",
- }
Genetic Programming entries for
Pablo Ramos Criado
Dolores Barrios Rolania
David de la Hoz Galiana
Daniel Manrique Gamo
Citations