Surrogate Genetic Programming: A semantic aware evolutionary search

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

  author =       "Ahmed Kattan and Yew-Soon Ong",
  title =        "Surrogate Genetic Programming: A semantic aware
                 evolutionary search",
  journal =      "Information Sciences",
  volume =       "296",
  pages =        "345--359",
  year =         "2015",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2014.10.053",
  URL =          "",
  abstract =     "Many semantic search based on Genetic Programming (GP)
                 use a trial-and-error scheme to attain semantically
                 diverse offspring in the evolutionary search. This
                 results in significant impediments on the success of
                 semantic-based GP in solving real world problems, due
                 to the additional computational overheads incurred.
                 This paper proposes a surrogate Genetic Programming (or
                 sGP in short) to retain the appeal of semantic-based
                 evolutionary search for handling challenging problems
                 with enhanced efficiency. The proposed sGP divides the
                 population into two parts (mu and lambda) then it
                 evolves mu percentage of the population using standard
                 GP search operators, while the remaining lambda
                 percentage of the population are evolved with the aid
                 of meta-models (or approximation models) that serve as
                 surrogate to the original objective function evaluation
                 (which is computationally intensive). In contrast to
                 previous works, two forms of meta-models are introduced
                 in this study to make the idea of using surrogate in GP
                 search feasible and successful. The first denotes a
                 {"}Semantic-model{"} for prototyping the semantic
                 representation space of the GP trees
                 (genotype/syntactic-space). The second is a
                 {"}Fitness-model{"}, which maps solutions in the
                 semantic space to the objective or fitness space. By
                 exploiting the two meta-models collectively in serving
                 as a surrogate that replaces the original problem
                 landscape of the GP search process, more cost-effective
                 generation of offspring that guides the search in
                 exploring regions where high quality solutions resides
                 can then be attained. Experimental studies covering
                 three separate GP domains, namely, (1) Symbolic
                 regression, (2) Even n-parity bit, and (3) a real-world
                 Time-series forecasting problem domain involving three
                 datasets, demonstrate that sGP is capable of attaining
                 reliable, high quality, and efficient performance under
                 a limited computational budget. Results also showed
                 that sGP outperformed the standard GP, GP based on
                 random training-set technique, and GP based on
                 conventional data-centric objectives as surrogate.",
  keywords =     "genetic algorithms, genetic programming, Semantic
                 space, Surrogate model, Semantic-model, Fitness-model,

Genetic Programming entries for Ahmed Kattan Yew-Soon Ong