Using Cultural Algorithms to Evolve Strategies in Agent-Based Models

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

  author =       "David A. Ostrowski and Troy Tassier and 
                 Mark P. Everson and Robert G. Reynolds",
  title =        "Using Cultural Algorithms to Evolve Strategies in
                 Agent-Based Models",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "741--746",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, agent-based
                 models, agent-based simulation, belief space, black box
                 testing, collective evolution process, cultural
                 algorithms, durable goods market, guided evolution,
                 heterogeneous population, parameter configurations,
                 population, pricing strategies, self-adaptive models,
                 simulated real-world market scenario, software
                 engineering techniques, strategy evolution, successive
                 simulations, transactions, white box testing, belief
                 maintenance, costing, digital simulation, economics,
                 evolutionary computation, financial data processing,
                 multi-agent systems, software engineering,",
  DOI =          "doi:10.1109/CEC.2002.1007018",
  abstract =     "Cultural Algorithms are self-adaptive models that
                 support the collective evolution process through the
                 employment of a population and a belief space. Here,
                 the Cultural approach is applied to derive a
                 generalized set of beliefs from successive populations
                 of parameter configurations from an agent-based
                 simulation of transactions within a durable goods
                 market. The maintenance of this information allows for
                 the guided evolution of the agent-based system over
                 successive simulations. In order to more effectively
                 evaluate parameter configurations, Software Engineering
                 techniques of white and black box testing are applied.
                 In this paper, a methodology for the use of Cultural
                 Algorithms to optimize strategies in agent-based models
                 is presented. This approach is demonstrated in an
                 application used to model pricing strategies in the
                 context of an agent-based model under a simulated
                 real-world market scenario and a heterogeneous

Genetic Programming entries for David A Ostrowski Troy Tassier Mark P Everson Robert G Reynolds