Summary
Many real-world optimization problems involve multiple, typically conflicting objectives. Often, it is very difficult to weigh the different criteria exactly before alternatives are known. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions. However, often the user has at least a vague idea about what kind of solutions might be preferred. In this chapter, we argue that such knowledge should be used to focus the search on the most interesting (from a user’s perspective) areas of the Paretooptimal front. To this end, we present and compare two methods which allow to integrate vague user preferences into evolutionary multi-objective algorithms. As we show, such methods may speed up the search and yield a more fine-grained selection of alternatives in the most relevant parts of the Pareto-optimal front.
This is a preview of subscription content, log in via an institution to check access.
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
J. Branke, T. Kaußler, and H. Schmeck. Guidance in evolutionary multiobjective optimization. Advances in Engineering Software, 32:499–507, 2001.
C. A. Coello Coello. Handling preferences in evolutionary multiobjective optimization: A survey. In Congress on Evolutionary Computation, volume 1, pages 30–37. IEEE, 2000.
Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, 2002.
Dragan Cvetkovic and Ian C. Parmee. Preferences and their application in evolutionary multiobjective optimisation. IEEE Transactions on Evolutionary Computation, 6(1):42–57, 2002.
K. Deb. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation Journal, 7(3):205–230, 1999.
K. Deb. Solving goal programming problems using multi-objective genetic algorithms. In Proceedings of Congress on Evolutionary Computation, pages 77–84, 1999.
K. Deb. Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley, 2001.
K. Deb. Multi-objective evolutionary algorithms: Introducing bias among Pareto-optimal solutions. In A. Ghosh and S. Tsutsui, editors, Advances in Evolutionary Computing: Theory and Applications, pages 263–292. London: Springer-Verlag, 2003.
K. Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Systems, 9(2):115–148, 1995.
K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
K. Deb and M. Goyal. A robust optimization procedure for mechanical component design based on genetic adaptive search. Transactions of the ASME: Journal of Mechanical Design, 120(2):162–164, 1998.
K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable multi-objective optimization test problems. In Proceedings of the Congress on Evolutionary Computation (CEC-2002), pages 825–830, 2002.
C. M. Fonseca and P. J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, 1993.
Yaochu Jin, Tatsuya Okabe, and Bernhard Sendhoff. Adapting weighted aggregation for multiobjective evolution strategies. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, Evolutionary Multi-criterion Optimization, number 1993 in LNCS, pages 96–110. Springer, 2001.
Yaochu Jin and Bernhard Sendhoff. Fuzzy preference incorporation into evolutionary multi-objective optimization. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, volume 1, pages 26–30, Singapore, November 2002.
D. Van Veldhuizen and Gary B. Lamont. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation Journal, 8(2):125–148, 2000.
Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation Journal, 8(2):125–148, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Branke, J., Deb, K. (2005). Integrating User Preferences into Evolutionary Multi-Objective Optimization. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-44511-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-06174-5
Online ISBN: 978-3-540-44511-1
eBook Packages: EngineeringEngineering (R0)