Abstract
Multiobjective optimization and decision making are strongly inter-related. This paper presents an interactive approach for the integration of expert preferences into multi-objective evolutionary optimization. The experts underlying preference is modeled only based on comparative queries that are designed to distinguish among the non-dominant solutions with minimal burden on the decision maker. The preference based approach constitutes a compromise between global approximation of a Pareto front and aggregation of objectives into a scalar utility function. The model captures relevant aspects of multi-objective decision making, such as preference handling, ambiguity and incommensurability. The efficiency of the approach in terms of number of expert decisions and convergence to the optimal solution are analyzed on the basis of an artificial decision behavior with respect to optimization benchmarks.
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
Coello, C.A.C.: Handling preferences in evolutionary multiobjective optimization: A survey. In: Proceedings of the CEC 2000, pp. 30–37 (2000)
Branke, J., Kaußler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32(6), 499–507 (2001)
Chaudhuri, S., Deb, K.: An interactive evolutionary multi-objective optimization and decision making procedure. Applied Soft Computing 10(2), 496–511 (2010)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. LNCS, pp. 849–858. Springer, Heidelberg (2000)
Fernandez, E., Lopez, E., Bernal, S., Coello Coello, C.A., Navarro, J.: Evolutionary multiobjective optimization using an outranking-based dominance generalization. Comput. Oper. Res. 37(2), 390–395 (2010)
Parmee, I.C., Cvetkovic, D., Watson, A., Bonham, C.: Multiobjective satisfaction within an interactive evolutionary design environment. Evolutionary Computation 8(2), 197–222 (2000)
Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of ec optimization and human evaluation. Proc. of the IEEE 89(9), 1275–1296 (2001)
Krettek, J., Braun, J., Hoffmann, F., Bertram, T.: Interactive incorporation of user preferences in multiobjective evolutionary algorithms. Applications of Soft Computing 58, 379–388
Fonseca, C., Fleming, P.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms, January 1993, pp. 416–423 (1993)
Kursawe, F.: A variant of evolution strategies for vector optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krettek, J., Braun, J., Hoffmann, F., Bertram, T. (2010). Preference Modeling and Model Management for Interactive Multi-objective Evolutionary Optimization. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-14049-5_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14048-8
Online ISBN: 978-3-642-14049-5
eBook Packages: Computer ScienceComputer Science (R0)