Integrating Local Search within neat-GP

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

  author =       "Perla Juarez-Smith and Leonardo Trujillo",
  title =        "Integrating Local Search within {neat-GP}",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "993--996",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931659",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "There are two important limitations of standard
                 tree-based genetic programming (GP). First, GP tends to
                 evolve unnecessarily large programs, what is referred
                 to as bloat. Second, it uses inefficient search
                 operators that operate at the syntax level. The first
                 problem has been the subject of a fair amount of
                 research over the years. Regarding the second problem,
                 one approach is to use alternative search operators,
                 for instance geometric semantic operators. However,
                 another approach is to introduce greedy local search
                 strategies, combining the syntactic search performed by
                 standard GP with local search strategies for solution
                 tuning, which is a simple strategy that has
                 comparatively received much less attention. This work
                 combines a recently proposed bloat-free GP called
                 neat-GP with a local search strategy. One benefit of
                 using a bloat-free GP is that it reduces the size of
                 the parameter space confronted by the local searcher,
                 offsetting some of the added computational cost. The
                 algorithm is validated on a real-world problem with
                 promising results.",
  notes =        "GECCO Student Workshop, Best Paper Award 2nd

                 Distributed at GECCO-2016.",

Genetic Programming entries for Perla Sarahi Juarez-Smith Leonardo Trujillo