Communications of the ACM
A guided tour of Chernoff bounds
Information Processing Letters
Parallel algorithms and architectures for very fast AI search
Parallel algorithms and architectures for very fast AI search
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
New local search approximation techniques for maximum generalized satisfiability problems
CIAC '94 Proceedings of the second Italian conference on Algorithms and complexity
Approximate solution of NP optimization problems
Theoretical Computer Science
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
Heuristics for Improving the Non-oblivious Local Search for MaxSAT
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Improved Exact Algorithms for MAX-SAT
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Some Prospects for Efficient Fixed Parameter Algorithms
SOFSEM '98 Proceedings of the 25th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Critical Parallelization of Local Search for MAX-SAT
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
ICAL '99 Proceedings of the 26th International Colloquium on Automata, Languages and Programming
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Worst-case study of local search for MAX-k-SAT
Discrete Applied Mathematics - The renesse issue on satisfiability
Solving weighted Max-Sat optimization problems using a Taboo Scatter Search metaheuristic
Proceedings of the 2004 ACM symposium on Applied computing
Efficient initial solution to extremal optimization algorithm for weighted MAXSAT problem
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
An effective heuristic algorithm for the maximum satisfiability problem
Applied Intelligence
GRASP with path relinking for the weighted MAXSAT problem
Journal of Experimental Algorithmics (JEA)
An ant algorithm for static and dynamic MAX-SAT problems
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
An efficient solver for weighted Max-SAT
Journal of Global Optimization
Wasp Swarm Algorithm for Dynamic MAX-SAT Problems
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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
Learning While Optimizing an Unknown Fitness Surface
Learning and Intelligent Optimization
Reinforcement Learning and Reactive Search: an adaptive MAX-SAT solver
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
MAX-2-SAT: how good is Tabu search in the worst-case?
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Iterated robust tabu search for MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Estimation of distribution algorithms with mutation
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
GRASP with path-relinking for the weighted maximum satisfiability problem
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Adaptive memory-based local search for MAX-SAT
Applied Soft Computing
Solving weighted MAX-SAT via global equilibrium search
Operations Research Letters
Global equilibrium search algorithms for combinatorial optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
A study of breakout local search for the minimum sum coloring problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Breakout Local Search for the Max-Cutproblem
Engineering Applications of Artificial Intelligence
A study of adaptive perturbation strategy for iterated local search
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
An iterated-tabu-search heuristic for a variant of the partial set covering problem
Journal of Heuristics
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The Reactive Search (RS) method proposes the integration of a simple history-sensitive (machine learning) scheme into local search for the on-line determination of free parameters. In this paper a new RS algorithm is proposed for the approximated solution of the Maximum Satisfiability problem: a component based on local search with temporary prohibitions (Tabu Search) is complemented with a reactive scheme that determines the appropriate value of the prohibition parameter by monitoring the Hamming distance along the search trajectory. The proposed algorithm (H-RTS) can therefore be characterized as a dynamic version of Tabu Search.In addition, the non-oblivious functions recently introduced in the framework of approximation algorithms are used to discover a better local optimum in the initial part of the searchThe algorithm is developed in two phases. First the bias-diversification properties of individual candidate components are analyzed by extensive empirical evaluation, then a reactive scheme is added to the winning component, based on Tabu Search.The final tests on a benchmark of random MAX-3-SAT and MAX-4-SAT problems demonstrate the superiority of H-RTS with respect to alternative heuristics.