Shared memory based Cooperative Coevolution

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

@InProceedings{puppala:1998:smbcc,
  author =       "Narendra Puppala and Sandip Sen and Maria Gordin",
  title =        "Shared memory based Cooperative Coevolution",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "570--574",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Applications
                 of Evolutionary Computation, Representation and
                 Operators, Comparing Algorithms, agent behaviours,
                 autonomous agents, behavioural strategies, coordination
                 skills, multiagent systems, optimal behaviour patterns,
                 room painting domain, shared memory, shared memory
                 based cooperative coevolution, cooperative systems,
                 software agents",
  ISBN =         "0-7803-4869-9",
  file =         "c098.pdf",
  DOI =          "doi:10.1109/ICEC.1998.700091",
  size =         "5 pages",
  abstract =     "Autonomous agents that possess distinct expertise but
                 lack proper coordination skills can suffer from poor
                 performance in a cooperative setting. The success of
                 agents in multiagent systems is based on their ability
                 to adapt effectively with other agents in completing
                 their tasks. We present here a co-evolutionary approach
                 to generating behavioral strategies for autonomous
                 agents cooperating with each other to achieve a common
                 goal. We co-evolve agent behaviors with genetic
                 algorithms (GAS) where one GA population is evolved per
                 individual in the cooperative group. Groups are formed
                 by pairing strategies from each population and the best
                 pairs are stored in shared memory. Population members
                 are evaluated by pairing them with representatives of
                 other populations in the shared memory. Experimental
                 results obtained by conducting experiments in a room
                 painting domain are presented, showing the success of
                 the shared memory approach in consistently generating
                 optimal behavior patterns. Performance comparisons with
                 a random pairing approach and a single population
                 approach demonstrate the utility of the shared memory
                 approach.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence. Presented
                 at WCCI-98 by Dale A. Schoenefeld. Painter and
                 Whitewasher problem",
}

Genetic Programming entries for Narendra Puppala Sandip Sen Maria Gordin

Citations