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 DaEYAHSP are evaluated on some large instances with singular Pareto Front shapes, and compared to those of the single-objective aggregation-based approach.
Cite
CITATION STYLE
Quemy, A., Schoenauer, M., Vidal, V., Dréo, J., & Savéant, P. (2015). Solving large MultiZenoTravel benchmarks with Divide-and-Evolve. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8994, pp. 262–267). Springer Verlag. https://doi.org/10.1007/978-3-319-19084-6_25
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
