Semantically-based crossover in genetic programming: application to real-valued symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Quang:2011:GPEM,
  author =       "Nguyen Quang Uy and Nguyen Xuan Hoai and 
                 Michael O'Neill and R. I. McKay and Edgar Galvan-Lopez",
  title =        "Semantically-based crossover in genetic programming:
                 application to real-valued symbolic regression",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2011",
  volume =       "12",
  number =       "2",
  pages =        "91--119",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Crossover, Symbolic regression, locality",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-010-9121-2",
  size =         "29 pages",
  abstract =     "We investigate the effects of semantically-based
                 crossover operators in genetic programming, applied to
                 real-valued symbolic regression problems. We propose
                 two new relations derived from the semantic distance
                 between subtrees, known as semantic equivalence and
                 semantic similarity. These relations are used to guide
                 variants of the crossover operator, resulting in two
                 new crossover operators-semantics aware crossover (SAC)
                 and semantic similarity-based crossover (SSC). SAC, was
                 introduced and previously studied, is added here for
                 the purpose of comparison and analysis. SSC extends SAC
                 by more closely controlling the semantic distance
                 between subtrees to which crossover may be applied. The
                 new operators were tested on some real-valued symbolic
                 regression problems and compared with standard
                 crossover (SC), context aware crossover (CAC), Soft
                 Brood Selection (SBS), and No Same Mate (NSM)
                 selection. The experimental results show on the
                 problems examined that, with computational effort
                 measured by the number of function node evaluations,
                 only SSC and SBS were significantly better than SC, and
                 SSC was often better than SBS. Further experiments were
                 also conducted to analyse the performance sensitivity
                 to the parameter settings for SSC. This analysis leads
                 to a conclusion that SSC is more constructive and has
                 higher locality than SAC, NSM and SC; we believe these
                 are the main reasons for the improved performance of
                 SSC.",
  affiliation =  "University College Dublin Complex & Adaptive
                 Systems Lab, School of Computer Science &
                 Informatics Dublin Ireland",
}

Genetic Programming entries for Quang Uy Nguyen Nguyen Xuan Hoai Michael O'Neill R I (Bob) McKay Edgar Galvan Lopez

Citations