Using cultural algorithms to evolve strategies in agent-based models

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

@PhdThesis{Ostrowski:thesis,
  author =       "David Alfred Ostrowski",
  title =        "Using cultural algorithms to evolve strategies in
                 agent-based models",
  school =       "Wayne State University",
  year =         "2002",
  address =      "Detroit, Michigan, USA",
  month =        mar,
  note =         "AAI3047580",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://digitalcommons.wayne.edu/dissertations/AAI3047580/",
  URL =          "http://digitalcommons.wayne.edu/dissertations/AAI3047580.pdf",
  URL =          "http://ai.cs.wayne.edu/ai/dissertations.htm",
  URL =          "http://genealogy.math.ndsu.nodak.edu/id.php?id=100306",
  size =         "xii + 208 pages",
  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.",
  notes =        "ADVISER Reynolds, Robert G.

                 ISBN 978-0-493-62085-5

                 SOURCE DAI-B 63/03, p. 1432, Sep 2002",
}

Genetic Programming entries for David A Ostrowski

Citations