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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.

Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve