Lead user analyses for the development of new industrial products
Management Science
Fast Polyhedral Adaptive Conjoint Estimation
Marketing Science
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We propose a customizable C2C (customer to customer) framework to fully utilize interactive genetic algorithms (IGA) and to discover the potential capabilities of IGAs in customer designs. Traditionally, IGA users assign fitness to each chromosome. No matter the rating or ranking of the assignments, the traditional methods were unnatural, especially when IGAs were applied to customers’ designs. In this study, we find that allowing IGA users to directly select chromosomes into the mating pool according to their hidden fitness function(s) is not only a natural way to implement the select operations of IGA, but is also more effective. We call the model where parts of select operations are manipulated by users, the SIGA model. Preventing fatigue, however, is the most important challenge in IGA. The OIGA (Over-sampling IGA) model has been extremely effective at decreasing user fatigue. To verify the performance of the proposed SIGA, we conduct a case study and use the OIGA model as a benchmark. The results of the case study show that the proposed SIGA model is significantly more effective than the IOGA model.