Efficiently evolving programs through the search for novelty

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Lehman:2010:gecco,
  author =       "Joel Lehman and Kenneth O. Stanley",
  title =        "Efficiently evolving programs through the search for
                 novelty",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "837--844",
  keywords =     "genetic algorithms, genetic programming, premature
                 convergence, program bloat, Artificial Intelligence,
                 Learning, Novelty search",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830638",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.4409",
  URL =          "http://eplex.cs.ucf.edu/papers/lehman_gecco10b.pdf",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A significant challenge in genetic programming is
                 premature convergence to local optima, which often
                 prevents evolution from solving problems. This paper
                 introduces to genetic programming a method that
                 originated in neuroevolution (i.e. the evolution of
                 artificial neural networks) that circumvents the
                 problem of deceptive local optima. The main idea is to
                 search only for behavioural novelty instead of for
                 higher fitness values. Although such novelty search
                 abandons following the gradient of the fitness
                 function, if such gradients are deceptive they may
                 actually occlude paths through the search space towards
                 the objective. Because there are only so many ways to
                 behave, the search for behavioral novelty is often
                 computationally feasible and differs significantly from
                 random search. Counter intuitively, in both a deceptive
                 maze navigation task and the artificial ant benchmark
                 task, genetic programming with novelty search, which
                 ignores the objective, outperforms traditional genetic
                 programming that directly searches for optimal
                 behaviour. Additionally, novelty search evolves smaller
                 program trees in every variation of the test domains.
                 Novelty search thus appears less susceptible to bloat,
                 another significant problem in genetic programming. The
                 conclusion is that novelty search is a viable new tool
                 for efficiently solving some deceptive problems in
                 genetic programming.",
  notes =        "Maze, artificial ant (Santa Fe, Los Altos). LilGP.
                 p838 'novelty search ... ignores the objective'. p839
                 'novelty needs to be measured'. k-nearest (k=25)
                 phenotype clustering (cf fitness sharing, hall-of-fame
                 coevolution archive, tabu) Mouret. Fitness = Euclidean
                 distance between time sequences of food collection
                 (fixed length vectors?) Pop=1000, 1000 generations,
                 binary tournaments. Santa Fe Ant Wrong number of time
                 steps. Performance similar to GP on Ant, much better on
                 hardest maze.

                 Also known as \cite{1830638} GECCO-2010 A joint meeting
                 of the nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",
}

Genetic Programming entries for Joel Lehman Kenneth O Stanley

Citations