Learning While Optimizing an Unknown Fitness Surface
Learning and Intelligent Optimization
Computers and Operations Research
Autonomous Control Approach for Local Search
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Reactive and dynamic local search for max-clique: Engineering effective building blocks
Computers and Operations Research
Interfaces
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Grapheur: a software architecture for reactive and interactive optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Reasoning with optional and preferred requirements
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
A heuristic algorithm for a prize-collecting local access network design problem
INOC'11 Proceedings of the 5th international conference on Network optimization
Efficient multi-start strategies for local search algorithms
Journal of Artificial Intelligence Research
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
Communications of the ACM
Active learning of combinatorial features for interactive optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
HyFlex: a benchmark framework for cross-domain heuristic search
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
An exploration-exploitation compromise-based adaptive operator selection for local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An algorithm comparison for dynamic optimization problems
Applied Soft Computing
Breakout Local Search for maximum clique problems
Computers and Operations Research
The Gestalt heuristic: emerging abstraction to improve combinatorial search
Natural Computing: an international journal
Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Breakout Local Search for the Max-Cutproblem
Engineering Applications of Artificial Intelligence
The vehicle routing problem with restricted mixing of deliveries and pickups
Journal of Scheduling
Search methodologies in real-world software engineering
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey on optimization metaheuristics
Information Sciences: an International Journal
Bio-inspired optimization techniques: a critical comparative study
ACM SIGSOFT Software Engineering Notes
Learning and intelligent optimization (LION): one ring to rule them all
Proceedings of the VLDB Endowment
Iterative-deepening search with on-line tree size prediction
Annals of Mathematics and Artificial Intelligence
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Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.