Successful product development depends on many factors. Among the most important factors are identification and satisfaction of customers' perceived needs, the accessibility, size and growth rate of the target market, and of course, production costs. Since the early 1970s marketing researchers achieved remarkable results in developing methods to measure consumer prererences of multiattributed products. Additionally, market segmentation methods have been an important issue in strategic marketing research. This study, however, concentrates on a new method of product design optimization. It is shown how genetic algorithms are used to simultaneously discover optimal multi-attributed products for different customer preferences. For that purpose we chose an interactive version of the genetic algorithm where genetic operators like selection, mutation and crossover are applied as usual. The use of the interactive genetic algorithm is most suitable, where measures of utility are difficult or impossible to specify mathematically. Imprecise optimization in terms of a prioriunknown individual consumer decision rules and preferences in an important issue for marketing researchers. The interactive genetic algotithm tries to solve design problems in that the cosumer plays the role of the objective function during data collection.
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