Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Machine Learning
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Boosting Verification by Automatic Tuning of Decision Procedures
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
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
COMPOSER: a probabilistic solution to the utility problem in speed-up learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
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
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
Communications of the ACM
Random search for hyper-parameter optimization
The Journal of Machine Learning Research
Meta-optimization for parameter tuning with a flexible computing budget
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Automatically configuring algorithms for scaling performance
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Ordered racing protocols for automatically configuring algorithms for scaling performance
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multi-objective optimization with surrogate trees
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An analysis of post-selection in automatic configuration
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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
Lazy paired hyper-parameter tuning
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 hard computational problems often expose many parameters that can be modified to improve empirical performance. However, manually exploring the resulting combinatorial space of parameter settings is tedious and tends to lead to unsatisfactory outcomes. Recently, automated approaches for solving this algorithm configuration problem have led to substantial improvements in the state of the art for solving various problems. One promising approach constructs explicit regression models to describe the dependence of target algorithm performance on parameter settings; however, this approach has so far been limited to the optimization of few numerical algorithm parameters on single instances. In this paper, we extend this paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances. We experimentally validate our new algorithm configuration procedure by optimizing a local search and a tree search solver for the propositional satisfiability problem (SAT), as well as the commercial mixed integer programming (MIP) solver CPLEX. In these experiments, our procedure yielded state-of-the-art performance, and in many cases outperformed the previous best configuration approach.