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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.
List of contents
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.
Summary
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.
