Abstract
In order to understand strengths and weaknesses of optimization algorithms, it is important to have access to different types of test problems, well defined performance indicators and analysis tools. Such tools are widely available for testing evolutionary multiobjective optimization algorithms.
To our knowledge, there do not exist tools for analyzing the performance of interactive multiobjective optimization methods based on the reference point approach to communicating preference information. The main barrier to such tools is the involvement of human decision makers into interactive solution processes, which makes the performance of interactive methods dependent on the performance of humans using them. In this research, we aim towards a testing framework where the human decision maker is replaced with an artificial one and which allows to repetitively test interactive methods in a controlled environment.
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Notes
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Humans learn, therefore, it is not easy to employ the same DMs to test different methods, as they have learnt about the problem while solving the problem, which affects the quality of a long series of experiments.
References
Babbar-Sebens, M., Minsker, B.S.: Interactive genetic algorithm with mixed initiative interaction for multi-criteria ground water monitoring design. Appl. Soft Comput. 12(1), 182–195 (2012)
Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.): Multiobjective Optimization: Interactive and Evolutionary Approaches. Springer, Heidelberg (2008)
Deb, K., Miettinen, K., Chaudhuri, S.: Towards an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Trans. Evol. Comput. 14(6), 821–841 (2010)
Deb, K., Sundar, J., Udaya Bhaskara Rao, N., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comput. Intell. Res. 2(3), 273–286 (2006)
Debreu, G.: Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Cowles Foundation for Research in Economics at Yale University, New Haven (1959). Monograph 17
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
López-Ibáñez, M., Knowles, J.: Machine decision makers as a laboratory for interactive EMO. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 295–309. Springer, Heidelberg (2015)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)
Miettinen, K., Hakanen, J., Podkopaev, D.: Interactive nonlinear multiobjective optimization methods. In: Greco, S., Ehrgott, M., Figueira, J. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 931–980. Springer, New York (2016)
Purshouse, R., Deb, K., Mansor, M., Mostaghim, S., Wang, R.: A review of hybrid evolutionary multiple criteria decision making methods. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp. 1147–1154 (2014)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation, and Application. Wiley, New York (1986)
Stewart, T.J.: Goal programming and cognitive biases in decision-making. J. Oper. Res. Soc. 56(10), 1166–1175 (2005)
Wierzbicki, A.: A mathematical basis for satisficing decision making. Math. Model. 3, 391–405 (1982)
Zujevs, A., Eiduks, J.: New decision maker model for multiobjective optimization interactive methods. In: Proceedings of the Information Technologies, pp. 51–58. Kaunas: Technologija (2011)
Acknowledgments
This work was supported on the part of Vesa Ojalehto by the Academy of Finland (grant number 287496).
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Ojalehto, V., Podkopaev, D., Miettinen, K. (2016). Towards Automatic Testing of Reference Point Based Interactive Methods. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_45
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