Using Cultural Algorithms to Evolve Strategies in Agent-Based Models
Created by W.Langdon from
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- @InProceedings{ostrowski:2002:ucatesiam,
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author = "David A. Ostrowski and Troy Tassier and
Mark P. Everson and Robert G. Reynolds",
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title = "Using Cultural Algorithms to Evolve Strategies in
Agent-Based Models",
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booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
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editor = "David B. Fogel and Mohamed A. El-Sharkawi and
Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
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pages = "741--746",
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year = "2002",
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publisher = "IEEE Press",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
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ISBN = "0-7803-7278-6",
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month = "12-17 " # may,
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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)",
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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,",
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DOI = "doi:10.1109/CEC.2002.1007018",
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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