Results 231 to 240 of about 1,200,755 (251)

Adaptive Thompson Sampling for hyper-heuristics

2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
There is an interest in search algorithms capable of learning and adapting their behaviour while solving a given problem. A hyper-heuristic operates on a set of predefined heuristics and applies a machine learning technique to predict which heuristic is the most effective to apply at a given point in time.
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Constructive Hyper-heuristics in Class Timetabling

2005 IEEE Congress on Evolutionary Computation, 2005
Evolutionary and stochastic problem solving techniques are often regarded with caution by end users because of their apparent fragility and slowness. Typically each problem is solved in isolation, and results are sensitive to parameters. Users may thus prefer easily understood but worse performing heuristic methods.
Peter Ross, Javier G. Marín-Blázquez
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A review of hyper-heuristics for educational timetabling

Annals of Operations Research, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A hyper-heuristic approach for the PDPTW

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
Amir Nasiri, Ed Keedwell, Raphaël Dorne, Mathias Kern, Gilbert Owusu   +4 more
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The importance of the learning conditions in hyper-heuristics

Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013
Evolutionary Algorithms are problem solvers inspired by nature. The effectiveness of these methods on a specific task usually depends on a non trivial manual crafting of their main components and settings. Hyper-Heuristics is a recent area of research that aims to overcome this limitation by advocating the automation of the optimization algorithm ...
Nuno Lourenço 0002, Francisco Baptista Pereira, Ernesto Costa   +2 more
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Hyper-heuristic Learning for Swarm Robots

2020
This thesis develops a framework for automatic construction of complex swarm robot behaviours. It demonstrates the benefit of machine learning in assisting swarm robots in unknown and dynamic environments.
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HHFS: Hyper-heuristic feature selection

Intelligent Data Analysis, 2016
Feature selection is an important machine learning field which can provide a key role for the challenging problem of classifying high-dimensional data. This problem is finding effective features among the set of all features in such that the final feature set can improve accuracy and reduce complexity.
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Hyper-heuristics tutorial

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2017
Daniel R. Tauritz, John R. Woodward
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Hyper-heuristics and cross-domain optimization

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, 2012
Hyper-heuristics comprise a set of approaches which are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies.
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Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

Applied Soft Computing Journal, 2020
Valdivino Alexandre de Santiago Junior, Ender Ozcan, Vinicius Renan DE CARVALHO   +2 more
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