Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Amplification of Search Performance through Randomization of Heuristics
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics
Journal of Heuristics
Computers and Operations Research
Learning from planner performance
Artificial Intelligence
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
Models and strategies for variants of the job shop scheduling problem
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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Test suites for many domains often fail to model features present in real-world problems. For the permutation flow-shop sequencing problem (PFSP), the most popular test suite consists of problems whose features are generated from a single uniform random distribution. Synthetic generation of problems with characteristics present in real-world problems is a viable alternative. We compare the performance of several competitive algorithms on problems produced with such a generator. We find that, as more realistic characteristics are introduced, the performance of a state-of-the-art algorithm degrades rapidly: faster and less complex stochastic algorithms provide superior performance. Our empirical results show that small changes in problem structure or problem size can influence algorithm performance. We hypothesize that these performance differences may be partially due to differences in search space topologies; we show that structured problems produce topologies with performance plateaus. Algorithm sensitivity to problem charaeteristics suggests the need to construct test suites more representative of real-world applications.