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Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms

Exploiting the Synergy of Software Engineering Knowledge in Evolutionary Design
  • David A. Ostrowski
  • Robert G. Reynolds
Part of the Genetic Programming Series book series (GPEM, volume 6)

Abstract

In this paper, we use Cultural Algorithms as a framework in which to embed a white and black box testing strategy for designing and testing large-scale GP programs. The model consists of two populations, one supports white box testing of a genetic programming system and the other supports black box testing. The two populations communicate by sending information to a shared belief space. This allows a potential synergy between the two activities. Next, we exploit this synergy in order to evolve an OEM pricing strategy in a complex agent-based market environment. The new pricing strategy generated over $2 million dollars in revenue during the assessment period and outperformed the previous optimal strategy.

Key words

Genetic Programming Cultural Algorithms Hybrid Genetic Programming Environments Agent-Based Modeling OEM strategy evolution Black box testing White Box Testing 

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • David A. Ostrowski
    • 1
  • Robert G. Reynolds
    • 2
  1. 1.Ford Motor Company Scientific Research LaboratoriesDearbornUSA
  2. 2.Dept. of Computer ScienceWayne State UniversityDetroitUSA

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