Adaptive and Multilevel Metaheuristics
Cotta, Carlos.
Adaptive and Multilevel Metaheuristics [electronic resource] / edited by Carlos Cotta, Marc Sevaux, Kenneth Sörensen. - XV, 275 p. online resource. - Studies in Computational Intelligence, 136 1860-949X ; . - Studies in Computational Intelligence, 136 .
Reviews of the Field -- Hyperheuristics: Recent Developments -- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation -- New Techniques and Applications -- An Efficient Hyperheuristic for Strip-Packing Problems -- Probability-Driven Simulated Annealing for Optimizing Digital FIR Filters -- RASH: A Self-adaptive Random Search Method -- Market Based Allocation of Transportation Orders to Vehicles in Adaptive Multi-objective Vehicle Routing -- A Simple Evolutionary Algorithm with Self-adaptation for Multi-objective Nurse Scheduling -- Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design -- Adaptive Estimation of Distribution Algorithms -- Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm -- Evolution of Descent Directions -- “Multiple Neighbourhood” Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods -- Automated Parameterisation of a Metaheuristic for the Orienteering Problem.
One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
9783540794387
10.1007/978-3-540-79438-7 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519
Adaptive and Multilevel Metaheuristics [electronic resource] / edited by Carlos Cotta, Marc Sevaux, Kenneth Sörensen. - XV, 275 p. online resource. - Studies in Computational Intelligence, 136 1860-949X ; . - Studies in Computational Intelligence, 136 .
Reviews of the Field -- Hyperheuristics: Recent Developments -- Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation -- New Techniques and Applications -- An Efficient Hyperheuristic for Strip-Packing Problems -- Probability-Driven Simulated Annealing for Optimizing Digital FIR Filters -- RASH: A Self-adaptive Random Search Method -- Market Based Allocation of Transportation Orders to Vehicles in Adaptive Multi-objective Vehicle Routing -- A Simple Evolutionary Algorithm with Self-adaptation for Multi-objective Nurse Scheduling -- Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design -- Adaptive Estimation of Distribution Algorithms -- Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm -- Evolution of Descent Directions -- “Multiple Neighbourhood” Search in Commercial VRP Packages: Evolving Towards Self-Adaptive Methods -- Automated Parameterisation of a Metaheuristic for the Orienteering Problem.
One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.
9783540794387
10.1007/978-3-540-79438-7 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519