Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
High-level optimization via automated statistical modeling
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Branch and bound algorithm selection by performance prediction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Machine Learning
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A perspective view and survey of meta-learning
Artificial Intelligence Review
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Exploiting Competitive Planner Performance
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
The Journal of Machine Learning Research
Boosting Verification by Automatic Tuning of Decision Procedures
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Probabilistically Estimating Backbones and Variable Bias: Experimental Overview
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Gaussian Process Models for Censored Sensor Readings
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
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
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Structural abstraction of software verification conditions
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Tuning the performance of the MMAS heuristic
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Hierarchical hardness models for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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
Online estimation of SAT solving runtime
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
ISAC --Instance-Specific Algorithm Configuration
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Monitoring the progress of anytime problem-solving
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Discovering the suitability of optimisation algorithms by learning from evolved instances
Annals of Mathematics and Artificial Intelligence
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Zchaff2004: an efficient SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Benchmarking SAT solvers for bounded model checking
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A learning-based algorithm selection meta-reasoner for the real-time MPE problem
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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
Random search for hyper-parameter optimization
The Journal of Machine Learning Research
An evaluation of machine learning in algorithm selection for search problems
AI Communications - The Symposium on Combinatorial Search
Evaluating component solver contributions to portfolio-based algorithm selectors
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
An analysis on separability for Memetic Computing automatic design
Information Sciences: an International Journal
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Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm@?s runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio-based algorithm selection, and the automatic configuration of parameterized algorithms. Over the past decade, a wide variety of techniques have been studied for building such models. Here, we describe extensions and improvements of existing models, new families of models, and-perhaps most importantly-a much more thorough treatment of algorithm parameters as model inputs. We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate these innovations through the largest empirical analysis of its kind, comparing to a wide range of runtime modelling techniques from the literature. Our experiments consider 11 algorithms and 35 instance distributions; they also span a very wide range of SAT, MIP, and TSP instances, with the least structured having been generated uniformly at random and the most structured having emerged from real industrial applications. Overall, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.