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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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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