Using Cultural Algorithms to Evolve Strategies in Agent-Based Models

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{ostrowski:2002:ucatesiam,

• 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 population.",

}

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

Citations