Evolving Board Evaluation Functions via Genetic Programming (Master’s Thesis)

For my Master’s thesis at Drexel University, I applied genetic programming techniques to the evolution of a strategy for evaluating potential moves in a one-step lookahead intelligent agent heuristic for a complex strategy-based game. The game I used is Acquire, whose object is to amass wealth while investing stock in hotel chains and effecting mergers of these chains as they grow. Genetic programming was used to evolve the board evaluation functions used by agent players of the game. The analysis of game interactions is recognized as a valid analogy to common real-world problems, which often present difficulty in designing algorithms to solve them. Genetic programming, as a branch of evolutionary computation, provides advantages over traditional algorithms in solving these complex real-world problems in speed, robustness and flexibility. The thesis contributed to work in artificial intelligence in providing computer systems with the tools they need to learn how to operate within a domain, given only the basic building blocks.

My thesis document:

  • Anthony, L. 2002. Evolving Board Evaluation Functions for a Complex Strategy Game. Master’s thesis, Department of Computer Science, Drexel University. December 2002. [pdf]

last revised 11/08/2012

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