Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
The OPL optimization programming language
The OPL optimization programming language
A Meta-Heuristic Factory for Vehicle Routing Problems
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Localizer: A Modeling Language for Local Search
Localizer: A Modeling Language for Local Search
Local Probing Applied to Scheduling
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A framework for constructing complete algorithms based on local search
AI Communications - Constraint Programming for Planning and Scheduling
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Combinatorial optimization in system configuration design
Automation and Remote Control
Hybrid algorithms in constraint programming
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
Using local search for guiding enumeration in constraint solving
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Benchmark comparisons tend to overlook the most important challenge in solving combinatorial problems: how to design an appropriate algorithm. For example, an early version of Localizer incurred a factor 3 performance penalty when benchmarked against a ‘C’ implementation of GSAT, but we would recommend implementing a new local search algorithm in Localizer rather than ‘C’ every time. The ECLiPSe CLP language supports the experimental process of seeking the right hybrid algorithm for the problem at hand. It offers high-level modelling and control features, extensibility and a wide range of constraint solvers which can cooperate in the solving of a problem. We recently sought a new hybrid algorithm for a very unpromising class (SAT problems), and using ECLiPSe we were able to develop an algorithm which showed good performance on some very hard instances. We describe the process of exploring the space of hybrid algorithms for the problem class, and indicate the features of ECLiPSe that enabled us to find previously undiscovered algorithms. How to benchmark the solving of this “meta-problem” remains a topic of future research. We conclude by pointing out the advantages of an extensible platform, such as ECLiPSe, for developing sophisticated hybrid algorithms for large scale industrial combinatorial optimisation problems.