Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
A study of the lot-sizing polytope
Mathematical Programming: Series A and B
Boosting Verification by Automatic Tuning of Decision Procedures
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
On the Use of Run Time Distributions to Evaluate and Compare Stochastic Local Search Algorithms
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Connections in networks: a hybrid approach
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
A gender-based genetic algorithm for the automatic configuration of algorithms
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
An empirical study of optimization for maximizing diffusion in networks
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
HAL: a framework for the automated analysis and design of high-performance algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
An evaluation of sequential model-based optimization for expensive blackbox functions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Bayesian optimization in high dimensions via random embeddings
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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State-of-the-art algorithms for solving hard computational problems often expose many parameters whose settings critically affect empirical performance. Manually exploring the resulting combinatorial space of parameter settings is often tedious and unsatisfactory. Automated approaches for finding good parameter settings are becoming increasingly prominent and have recently lead to substantial improvements in the state of the art for solving a variety of computationally challenging problems. However, running such automated algorithm configuration procedures is typically very costly, involving many thousands of invocations of the algorithm to be configured. Here, we study the extent to which parallel computing can come to the rescue. We compare straightforward parallelization by multiple independent runs with a more sophisticated method of parallelizing the model-based configuration procedure SMAC. Empirical results for configuring the MIP solver CPLEX demonstrate that near-optimal speedups can be obtained with up to 16 parallel workers, and that 64 workers can still accomplish challenging configuration tasks that previously took 2 days in 1---2 hours. Overall, we show that our methods make effective use of large-scale parallel resources and thus substantially expand the practical applicability of algorithm configuration methods.