Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Machine Learning - Special issue on learning in autonomous robots
Ant algorithms for discrete optimization
Artificial Life
Using global constraints for local search
DIMACS workshop on on Constraint programming and large scale discrete optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Journal of Artificial Intelligence Research
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Constraint-based agents: an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Tuning Local Search by Average-Reward Reinforcement Learning
Learning and Intelligent Optimization
Reinforcement Learning: A Tutorial Survey and Recent Advances
INFORMS Journal on Computing
Examination timetabling using late acceptance hyper-heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
A cooperative hyper-heuristic search framework
Journal of Heuristics
Cooperative solution to the vehicle routing problem
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Scheduling English football fixtures over the holiday period using hyper-heuristics
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Choosing the fittest subset of low level heuristics in a hyperheuristic framework
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Learning heuristic policies – a reinforcement learning problem
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 ant-based selection hyper-heuristic for dynamic environments
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
A new hyper-heuristic as a general problem solver: an implementation in HyFlex
Journal of Scheduling
A new methodology for the automatic creation of adaptive hybrid algorithms
Intelligent Data Analysis
| Hi-index | 0.00 |
Search decisions are often made using heuristic methods because real-world applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learns, during the search process, how to select promising heuristics. The learning is based on weight adaptation and can even switch between different heuristics during search. Different variants of the approach are evaluated within a constraint-programming environment.