Abstract
A method to generate various size tunable benchmarks for multi-objective AI planning with a known Pareto Front has been recently proposed in order to provide a wide range of Pareto Front shapes and different magnitudes of difficulty. The performance of the Pareto-based multi-objective evolutionary planner DaE \(_{\text {YAHSP}}\) are evaluated on some large instances with singular Pareto Front shapes, and compared to those of the single-objective aggregation-based approach.
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Bibaï, J., Savéant, P., Schoenauer, M., Vidal, V.: An evolutionary metaheuristic based on state decomposition for domain-independent satisficing planning. In: Brafman, R., et al. (eds.) 20th ICAPS, pp. 18–25. AAAI Press (2010)
Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)
Khouadjia, M.R., Schoenauer, M., Vidal, V., Dréo, J., Savéant, P.: Multi-objective AI planning: comparing aggregation and pareto approaches. In: Middendorf, M., Blum, C. (eds.) EvoCOP 2013. LNCS, vol. 7832, pp. 202–213. Springer, Heidelberg (2013)
Khouadjia, M.R., Schoenauer, M., Vidal, V., Dréo, J., Savéant, P.: Pareto-based multiobjective AI planning. In: Rossi, F. (eds.) Proceedings of the IJCAI. AAAI Press (2013)
Quemy, A., Schoenauer, M.: True Pareto Fronts for Multi-Objective AI Planning Instances (2015, submitted)
Schoenauer, M., Savéant, P., Vidal, V.: Divide-and-Evolve: a new memetic scheme for domain-independent temporal planning. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 247–260. Springer, Heidelberg (2006)
Sroka, M., Long, D.: Exploring metric sensitivity of planners for generation of pareto frontiers. In: Kersting, K., Toussaint, M. (eds.) 6th STAIRS, pp. 306–317. IOS Press (2012)
Zhang, Q., Hui, L.: A Multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
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Quemy, A., Schoenauer, M., Vidal, V., Dréo, J., Savéant, P. (2015). Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_25
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