Published as: Bruce Edmonds and Scott Moss and Helen
Gaylard (1997) Combining Evolutionary Computing Techniques to Find Credible
Qualitative Descriptions of the Demand-Side of Markets. Eufit '97-5th European
Congress on Intelligent Techniques and Soft Computing, pp. 727-731, Verlag
Mainz, Wissenschaftsverlag, 1997.
In many markets, customer preferences depend on the activity of the customer.
The authors have previously demonstrated a model that allows the testing
of the qualitative judgements of domain experts in spirits markets against
relevant EPOS price and sales data. This paper extends the use of context
dependent customer preferences to the case where domain experts lack confidence
in their judgements or the judgements are found not to be in accordance
with the data. We describe here an algorithm to produce credible alternative
models for the domain experts to confirm or develop in light of their wider
domain expertise. The algorithm combines random search, genetic programming
and evolutionary hill-climbing techniques. We report the results of tests
using data from a market for alcoholic beverages. The algorithm enables
the automatic production of qualitative descriptions which are both consistent
with observed data and deemed credible by the domain experts. The combination
of these techniques is more robust and produces better results than any
of these techniques separately.