Runtime Analysis of Mutation-Based Geometric Semantic Genetic Programming on Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Alberto Moraglio and Andrea Mambrini and 
                 Luca Manzoni",
  title =        "Runtime Analysis of Mutation-Based Geometric Semantic
                 Genetic Programming on Boolean Functions",
  booktitle =    "Foundations of Genetic Algorithms",
  year =         "2013",
  editor =       "Frank Neumann and Kenneth {De Jong}",
  pages =        "119--132",
  address =      "Adelaide, Australia",
  month =        "16-20 " # jan,
  organisation = "SigEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Boolean
                 functions, geometric crossover, runtime analysis,
  isbn13 =       "978-1-4503-1990-4",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2460239.2460251",
  acmid =        "2460251",
  size =         "13 pages",
  abstract =     "Geometric Semantic Genetic Programming (GSGP) is a
                 recently introduced form of Genetic Programming (GP),
                 rooted in a geometric theory of representations, that
                 searches directly the semantic space of
                 functions/programs, rather than the space of their
                 syntactic representations (e.g., trees) as in
                 traditional GP. Remarkably, the fitness landscape seen
                 by GSGP is always, for any domain and for any problem,
                 unimodal with a linear slope by construction. This has
                 two important consequences: (i) it makes the search for
                 the optimum much easier than for traditional GP; (ii)
                 it opens the way to analyse theoretically in a easy
                 manner the optimisation time of GSGP in a general
                 setting. The run time analysis of GP has been very hard
                 to tackle, and only simplified forms of GP on specific,
                 unrealistic problems have been studied so far. We
                 present a runtime analysis of GSGP with various types
                 of mutations on the class of all Boolean functions.",
  notes =        "Note change of author ordering. Also known as

                 Jan 2013 gsgp_foga13.pdf is preprint

Genetic Programming entries for Alberto Moraglio Andrea Mambrini Luca Manzoni