Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation. Proceedings of IEEE 89(9), 1275–1296 (2001)
Urban, G.L., von Hippel, E.: Lead User Analyses for the Development of New Industrial Products. Management Science 34(5), 569–582 (1988)
Thomke, S., von Hippel, E.: Customers as Innovators - a New Way to Create Value. Harvard Business Review 80, 74–81 (2002)
Dahan, E., Hauser, J.R.: The Virtual Customer. Journal of Product Innovation Management 19(5), 332–354 (2001)
Olivier, T., Hauser, J.R., Simester, D.I.: Polyhedral Methods for Adaptive Choice- Based Conjoint Analysis. Journal of Marketing Research 41(1), 116–131 (2004)
Olivier, T., Simester, D.I., Hauser, J.R., Dahan, E.: Fast Polyhedral Adaptive Conjoint Estimation. Marketing Science 22(3), 273–303 (2003)
Caldwell, C., Johnston, V.S.: Tracking a Criminal Suspect through Face-Space with a Genetic Algorithm. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 416–421. Morgan Kaufmann, San Francisco (1991)
Nishio, K., Murakami, M., Mizutani, E., Honda, N.: Fuzzy Fitness Assignment in an Interactive Genetic Algorithm for a Cartoon Face Search. In: Sanchez, E., Shibata, T., Zadeh, I.A. (eds.) Genetic Algorithms and Fuzzy Logic Systems - Soft Computing Perspectives, pp. 175–191. World Scientific Publishing, Singapore (1997)
Hsu, F.C., Chen, J.S.: A Study on Multi Criteria Decision Making Model: Interactive Genetic Algorithms Approach. In: Proceedings of the 1999 International Conference on SMC, Tokyo, Japan, pp. 634–639 (1999)
Hsu, F.C., Huang, P.: Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computations. New Generation Computing 23(2), 114–126 (2005)
Keeney, R.L.: Value-Focused Thinking: A path to Creative Decision-Making. Harvard University Press, Cambridge (1992)
Hung, M.H., Hsu, F.C.: Accelerating Interactive Evolutionary Computation Convergence Pace by Using Over-sampling Strategy. In: The Fourth IEEE International Workshop on Soft Computing as Trans-disciplinary Science and Technology, Muroran, Japan, May 25-27 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsu, FC., Hung, MH. (2006). Practically Applying Interactive Genetic Algorithms to Customers’ Designs on a Customizable C2C Framework: Entrusting Select Operations to IGA Users. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_55
Download citation
DOI: https://doi.org/10.1007/11732242_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
eBook Packages: Computer ScienceComputer Science (R0)