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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