An Empirical Comparison of Genetically Evolved Programs and Evolved Neural Networks for Multi-agent Systems Operating under Dynamic Environments

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@InProceedings{Davila:2015:GECCOcomp,
  author =       "Jaime J. Davila",
  title =        "An Empirical Comparison of Genetically Evolved
                 Programs and Evolved Neural Networks for Multi-agent
                 Systems Operating under Dynamic Environments",
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication of the 2015 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "1373--1374",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2764717",
  DOI =          "doi:10.1145/2739482.2764717",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper expands on the research presented in [12]
                 by comparing the performance of genetically evolved
                 programs operating under dynamic game environments with
                 that of neural networks with evolved weights. On the
                 genetic programming side, the maximum allowed tree
                 depth was varied in order to study its effect on the
                 evolutionary process. For evolution of neural networks,
                 encoding included direct encoding of weights and three
                 different L-Systems. Empirical results show that
                 genetic evolution of neural networks weights provided
                 better performance under dynamic environments when
                 evolved to choose which of several high-level actions
                 to perform, such as defend or attack. On the other
                 hand, genetic programming evolved better solutions for
                 low-level actions, such as move left, move right, or
                 accelerate. Solutions are analysed in order to explain
                 these differences.",
  notes =        "Also known as \cite{2764717} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Jaime J Davila

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