Solving the Lawn Mower problem with Kaizen Programming and $\lambda$-Linear Genetic Programming for Module Acquisition

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  author =       "Leo Francoso Dal Piccol Sotto and 
                 Vinicius Veloso {de Melo}",
  title =        "Solving the Lawn Mower problem with Kaizen Programming
                 and $\lambda$-Linear Genetic Programming for Module
  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",
  pages =        "113--114",
  keywords =     "genetic algorithms, genetic programming: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2909007",
  abstract =     "we have tested a new approach for evolving modular
                 programs: Kaizen Programming (KP) with epsilon-Linear
                 Genetic Programming (e-LGP) and a heuristic search
                 procedure to solve the well-known Lawn Mower problem.
                 KP is a novel hybrid approach that tries to efficiently
                 combine partial solutions to generate a high-quality
                 complete solution. Being a hybrid, KP may use different
                 types of methods to generate partial solutions, assess
                 their importance to the complete solution, and solve
                 the complete problem. Experiments on the Lawn Mower
                 problem show that the proposed method is effective in
                 finding the expected solution. It is a new alternative
                 for evolving modular programs, but further
                 investigations are necessary to improve its
  notes =        "Distributed at GECCO-2016.",

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo