Discriminating instance generation from abstract specifications : a case study with CP and MIP
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We extend automatic instance generation methods to allow cross-paradigm comparisons. We demonstrate that it is possible to completely automate the search for benchmark instances that help to discriminate between solvers. Our system starts from a high level human-provided problem specification, which is translated into a specification for valid instances. We use the automated algorithm configuration tool irace to search for instances, which are translated into inputs for both MIP and CP solvers by means of the Conjure, Savile Row, and MiniZinc tools. These instances are then solved by CPLEX and Chuffed, respectively. We constrain our search for instances by requiring them to exhibit a significant advantage for MIP over CP, or vice versa. Experimental results on four optimisation problem classes demonstrate the effectiveness of our method in identifying instances that highlight differences in performance of the two solvers.
Akgün , Ö , Dang , N , Miguel , I , Salamon , A Z , Spracklen , P & Stone , C 2020 , Discriminating instance generation from abstract specifications : a case study with CP and MIP . in E Hebrard & N Musliu (eds) , Integration of Constraint Programming, Artificial Intelligence, and Operations Research : 17th International Conference, CPAIOR 2020, Vienna, Austria, September 21–24, 2020, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12296 LNCS , Springer , Cham , pp. 41-51 , 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020 , Vienna, Online , Austria , 21/09/20 . https://doi.org/10.1007/978-3-030-58942-4_3conference
Integration of Constraint Programming, Artificial Intelligence, and Operations Research
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DescriptionThis work is supported by EPSRC grant EP/P015638/1 and used the Cirrus UK National Tier-2 HPC Service at EPCC funded by the University of Edinburgh and EPSRC (EP/P020267/1).
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