Results 221 to 230 of about 3,855 (254)

Generative Hyper-heuristics

Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2022
Daniel R. Tauritz, John R. Woodward
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Advances in Hyper-Heuristics

2018
The previous chapters have introduced the four types of hyper-heuristics, presented the theoretical foundations and examined various applications of hyper-heuristics. This chapter provides an overview of some advanced topics and recent trends in hyper-heuristics, namely, hybrid hyper-heuristics, hyper-heuristics for automated design, automated design ...
Nelishia Pillay, Rong Qu
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Comparing hyper-heuristics with blackboard systems

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
This paper aims to draw a comparison between the traditional view of hyper-heuristics and a lesser known type of multi-agent system known as a blackboard system. Both approaches share many similarities in both implementation and philosophy but also have several important differences in terms of characteristics and approach, such as a difference in ...
Kevin Carrie Graham, Leslie Smith
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Evolutionary multilabel hyper-heuristic design

2017 IEEE Congress on Evolutionary Computation (CEC), 2017
Nowadays, heuristics represent a commonly used alternative to solve complex optimization problems. This, however, has given rise to the problem of choosing the most effective heuristic for a given problem. In recent years, one of the most used strategies for this task has been the hyper-heuristics, which aim at selecting/generating heuristics to solve ...
Alejandro Rosales-Pérez, Andrés Eduardo Gutiérrez-Rodríguez, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, Carlos A. Coello Coello   +4 more
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Selection hyper-heuristics

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
Ahmed Kheiri, Edward C. Keedwell
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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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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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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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