Using cultural algorithms to evolve strategies in agent-based models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @PhdThesis{Ostrowski:thesis,
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author = "David Alfred Ostrowski",
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title = "Using cultural algorithms to evolve strategies in
agent-based models",
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school = "Wayne State University",
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year = "2002",
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address = "Detroit, Michigan, USA",
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month = mar,
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note = "AAI3047580",
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keywords = "genetic algorithms, genetic programming",
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broken = "http://digitalcommons.wayne.edu/dissertations/AAI3047580/",
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URL = "http://digitalcommons.wayne.edu/dissertations/AAI3047580.pdf",
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broken = "http://ai.cs.wayne.edu/ai/dissertations.htm",
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URL = "http://genealogy.math.ndsu.nodak.edu/id.php?id=100306",
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size = "xii + 208 pages",
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abstract = "Software Engineering methodologies have demonstrated
their importance in the efficient solution of complex
real-world problems. The process of software
development can be viewed as searching through the
state space of all possible programs. Evolutionary
computation methods are useful in this search process
due to their higher level of complexity. We are
interested in performing an efficient search through
the leverage is of Software Engineering techniques in
order to maintain detailed information about program
constraints. Our goal is to focus the search through
identification of these constraints. This thesis takes
software testing methodologies and applies them to
software design. Software testing processes reinforce
and verify the design by the practice of determining
program faults through the identification of knowledge
that can allow the programmer to pin-point its cause
and relate them back to the specification. We rely on
complementary approaches in Software testing which are
white box and black box testing. White box testing
examines a programs structure while black box examines
outputs in relevance to input data sets. These are
applied in the context of software design in which the
white box is first applied in order to generate a
prototype. Once the program has been developed to a
suitable level of performance, a black box approach is
applied. This process runs in sequence until a suitable
solution is found. We apply these testing concepts
through the use of Cultural Algorithms. Cultural
Algorithms enhance the evolutionary process through the
application of a belief structure to the traditional
evolutionary approach. Our approach two Cultural
Algorithms with one focusing on white box and the
second on black box. This is termed as a Dual Cultural
Algorithms with Genetic Programming. We apply this to a
benchmark problem, the quadratic equation, which has
initially been used by Zannoni and Reynolds [Zannoni
1996]. Here, we present a more effective approach in
program generation in comparison to a standard GP
approach. The solutions generated are also demonstrated
to less complex than those generated with standard GP
approaches. Next, we apply this to a multi-agent system
developed in order to simulate transactions in a
durable goods market. Here, we find that a near-optimal
strategy has a diminishing effect when heterogeneous
factors are applied to our agents. We use the DCAGP
framework to calibrate our agent-based model by
allowing it to use the multi-agent system by allowing
the evolutionary framework to use the multi-agent
system as a performance function. This approach allows
us to produce a near optimal solution in less
generations than standard genetic programming
methodologies.",
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notes = "ISBN 978-0-493-62085-5
SOURCE DAI-B 63/03, p. 1432, Sep 2002
Supervisor: Robert G. Reynolds",
- }
Genetic Programming entries for
David A Ostrowski
Citations