Grammatical Evolution in Dynamic Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ian Dempsey",
  title =        "Grammatical Evolution in Dynamic Environments",
  school =       "University College Dublin",
  year =         "2007",
  address =      "Ireland",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, dynamic environments",
  abstract =     "Many real-world problems are anchored in dynamic
                 environments, where some element of the problem domain
                 changes with time. The application of Evolutionary
                 Computation (EC) to dynamic environments creates
                 challenges different to those encountered in static
                 environments. Foremost among these, are issues of
                 premature convergence, and the evolution of overfit
                 solutions. This study aims to identify mechanisms that
                 address these problems. A recent powerful addition to
                 the stable of EC methodologies is Grammatical Evolution
                 (GE). GE uses BNF grammars for the evolution of
                 variable length programs. Thus far, there has been
                 little study of the utility of GE in dynamic
                 environments. A comprehensive analysis of prior work in
                 EC and GE in the context of dynamic environments is
                 presented. From this, it is seen that GE offers
                 substantial potential due to the flexibility provided
                 by the BNF grammar and the many-to-one
                 genotype-to-phenotype mapping. Subsequently novel
                 methods of constant creation are introduced that
                 incorporate greater levels of latent evolvability
                 through the use of BNF grammars. These methods are
                 demonstrated to be more accurate and adaptable than the
                 standard methods adopted. Through placing GE in the
                 context of a dynamic real-world problem, the trading of
                 financial indices, phenotypic diversity is demonstrated
                 to be a function of the fitness landscape. That is,
                 phenotypic entropy fluctuates with the universe of
                 potentially fit solutions. Evidence is also presented
                 of the evolution of robust solutions that provide
                 superior out-of-sample performance over a statically
                 trained population. The findings in this study
                 highlight the importance of the genotype-to-phenotype
                 mapping for evolution in dynamic environments and
                 uncover some of the potential benefits of the
                 incorporation of BNF grammars in GE.",
  notes =        "See \cite{Dempsey:book}",

Genetic Programming entries for Ian Dempsey