Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
On SAT Instance Classes and a Method for Reliable Performance Experiments with SAT Solvers
Annals of Mathematics and Artificial Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Clause Weighting Local Search for SAT
Journal of Automated Reasoning
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Active learning with statistical models
Journal of Artificial Intelligence Research
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Using Cost Distributions to Guide Weight Decay in Local Search for SAT
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Analysis of adaptive operator selection techniques on the royal road and long k-path problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Restart Strategy Selection Using Machine Learning Techniques
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Bid evaluation in combinatorial auctions: optimization and learning
Software—Practice & Experience
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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
Predicting learnt clauses quality in modern SAT solvers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Performance prediction for RNA design using parametric and non-parametric regression models
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Designing and tuning SLS through animation and graphics: an extended walk-through
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
An integrated white+black box approach for designing and tuning stochastic local search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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
Advances in local search for satisfiability
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
On the generality of parameter tuning in evolutionary planning
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
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Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning When to Use Lazy Learning in Constraint Solving
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Self Controlling Tabu Search algorithm for the Quadratic Assignment Problem
Computers and Industrial Engineering
Instance-based parameter tuning for evolutionary AI planning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Images encryption by the use of evolutionary algorithms
Analog Integrated Circuits and Signal Processing
Instance-Specific algorithm configuration as a method for non-model-based portfolio generation
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Predicting good propagation methods for constraint satisfaction
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A meta-learning prediction model of algorithm performance for continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Models of performance of time series forecasters
Neurocomputing
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
Information Sciences: an International Journal
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Machine learning can be used to build models that predict the run-time of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make surprisingly accurate predictions of the run-time distributions of incomplete and randomized search methods, such as stochastic local search algorithms. We also show for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance. Empirical results for Novelty+ and SAPS on structured and unstructured SAT instances show very good predictive performance and significant speedups of our automatically determined parameter settings when compared to the default and best fixed distribution-specific parameter settings.