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.
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Ostrowski, D.A., Reynolds, R.G. (2003). Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_5
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DOI: https://doi.org/10.1007/978-1-4419-8983-3_5
Publisher Name: Springer, Boston, MA
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