Evolutionary organizational search

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/atal/LiYSM09,
  author =       "Boyang Li and Han Yu and Zhiqi Shen and Chunyan Miao",
  title =        "Evolutionary organizational search",
  booktitle =    "8th International Joint Conference on Autonomous
                 Agents and Multiagent Systems (AAMAS 2009)",
  year =         "2009",
  editor =       "Carles Sierra and Cristiano Castelfranchi and 
                 Keith S. Decker and Jaime Sim{\~a}o Sichman",
  volume =       "2",
  pages =        "1329--1330",
  address =      "Budapest, Hungary",
  month =        may # " 10-15",
  publisher =    "IFAAMAS",
  note =         "Extended Abstract",
  keywords =     "genetic algorithms, genetic programming, Poster,
                 Experimental; Systems, Biologically-Inspired
                 Approaches, Organizational Planning, Multi-Agent
                 Systems",
  isbn13 =       "978-0-9817381-7-8",
  URL =          "http://www.ifaamas.org/Proceedings/aamas09/pdf/02_Extended_Abstract/D_SP_0876.pdf",
  DOI =          "doi:10.1145/1558109.1558277",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.149.4762",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.4762",
  URL =          "http://www.ifaamas.org/Proceedings/aamas09/pdf/02_Extended_Abstract/D_SP_0876.pdf",
  abstract =     "In this paper, we proposed Evolutionary Organizational
                 Search (EOS), an optimization method for the
                 organizational control of multi-agent systems (MASs)
                 based on genetic programming (GP). EOS adds to the
                 existing armory a metaheuristic extension, which is
                 capable of efficient search and less vulnerable to
                 stalling at local optima than greedy methods due to its
                 stochastic nature. EOS employs a flexible genotype
                 which can be applied to a wide range of tree-shaped
                 organizational forms. EOS also considers special
                 constraints of MASs. A novel mutation operator, the
                 redistribution operator, was proposed. Experiments
                 optimizing an information retrieval system illustrated
                 the adaptation of solutions generated by EOS to
                 environmental changes.",
  notes =        "Poster:
                 http://www3.ntu.edu.sg/home/BYLI/paper/aamas_poster.pdf",
}

Genetic Programming entries for Boyang Li Han Yu Zhiqi Shen Chunyan Miao

Citations