Abstract
The trade-off solutions of a multi-objective optimization problem, as a whole, often hold crucial information in the form of rules. These rules, if predominantly present in most trade-off solutions, can be considered as the characteristic features of the Pareto-optimal front. Knowledge of such features, in addition to providing better insights to the problem, enables the designer to handcraft solutions for other optimization tasks which are structurally similar to it; thus eliminating the need to actually optimize. Innovization is the process of extracting these so called design rules. This paper proposes to move a step closer towards the complete automation of the innovization process through a niched clustering based optimization technique. The focus is on obtaining multiple design rules in a single knowledge discovery step using the niching strategy.
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References
Papalambros, P.Y., Wilde, D.J.: Principles of optimal design: Modeling and computation. Cambridge University Press, Cambridge (2000)
Deb, K., Srinivasan, A.: Monotonicity analysis, discovery of design principles, and theoretically accurate evolutionary multi-objective optimization. Journal of Universal Computer Science 13(7), 955–970 (2007)
Obayashi, S., Sasaki, D.: Visualization and data mining of pareto solutions using self-organizing map. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 796–809. Springer, Heidelberg (2003)
Obayashi, S., Jeong, S., Chiba, K.: Multi-objective design exploration for aerodynamic configurations. In: Proceedings of 35th AIAA Fluid Dynamics Conference and Exhibit, AIAA 2005-4666 (2005)
Ulrich, T., Brockhoff, D., Zitzler, E.: Pattern identification in Pareto-set approximations. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 737–744. ACM, New York (2008)
Oyama, A., Nonomura, T., Fujii, K.: Data mining of Pareto-optimal transonic airfoil shapes using proper orthogonal decomposition. In: AIAA2009-4000. AIAA, Reston (2009)
Deb, K.: Unveiling innovative design principles by means of multiple conflicting objectives. Engineering Optimization 35(5), 445–470 (2003)
Deb, K., Srinivasan, A.: Innovization: Innovating design principles through optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 1629–1636. ACM, New York (2006)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, New York (2001)
Datta, D., Deb, K., Fonseca, C.: Multi-objective evolutionary algorithms for resource allocation problems. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 401–416. Springer, Heidelberg (2007)
Bittermann, M.: Personal communication (July 2010)
Madetoja, E., Ruotsalainen, H., Mönkkönen, V.: New visualization aspects related to intelligent solution procedure in papermaking optimization. In: EngOpt 2008 - International Conference on Engineering Optimization (2008)
Doncieux, S., Mouret, J., Bredeche, N.: Exploring new horizons in evolutionary design of robots. In: Workshop on Exploring new horizons in Evolutionary Design of Robots at IROS, pp. 5–12 (2009)
Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Bandaru, S., Deb, K.: Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design. In: IEEE Congress on Evolutionary Computation (CEC-2010), pp. 1224–1231. IEEE Press, Los Alamitos (2010)
Bandaru, S., Deb, K.: Towards automating the discovery of certain innovative design principles through a clustering based optimization technique. Engineering Optimization (in press), http://www.iitk.ac.in/kangal/papers/k2010001.pdf
Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament selection, niching, and the preservation of diversity. IlliGAL Report No. 91011, Urbana, IL: University of Illinois at Urbana-Champaign (1991)
Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000)
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Bandaru, S., Deb, K. (2011). Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_1
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DOI: https://doi.org/10.1007/978-3-642-19893-9_1
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