Results 211 to 220 of about 1,200,755 (251)
An iterated multi-stage selection hyper-heuristic [PDF]
European Journal of Operational Research, 2016 There is a growing interest towards the design of reusable general purpose search methods that are applicable to different problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods that
Ahmed Kheiri, Ender Ozcan
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A hyper-heuristic of scalarizing functions
Proceedings of the Genetic and Evolutionary Computation Conference, 2017Scalarizing functions have been successfully used by Multi-Objective Evolutionary Algorithms (MOEAs) for the fitness assignment process. Their popularity has to do with their low computational cost, their capability to generate (weakly) Pareto optimal solutions, and their effectiveness in solving many-objective optimization problems.
Raquel Hernández Gómez +1 more
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Template method hyper-heuristics
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014The optimization literature is awash with metaphorically-inspired metaheuristics and their subsequent variants and hybridizations. This results in a plethora of methods, with descriptions that are often polluted with the language of the metaphor which inspired them [8].
John Robert Woodward, Jerry Swan
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A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis, 2008Meta-heuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a meta-heuristic for solving a problem in a certain domain. Hyper-heuristics introduce a novel approach
Ender Özcan +2 more
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A hyper-heuristic clustering algorithm
2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros
Chun-Wei Tsai +2 more
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2018
This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas.
Epitropakis, M.G., Burke, E.K.
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This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas.
Epitropakis, M.G., Burke, E.K.
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New Insights Into Diversification of Hyper-Heuristics
IEEE Transactions on Cybernetics, 2014There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our
Zhilei Ren +4 more
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An investigation of hyper-heuristic search spaces
2007 IEEE Congress on Evolutionary Computation, 2007Hyper-heuristics or "heuristics that coordinate heuristics" are fast becoming popular for solving combinatorial optimisation problems. These methods do not search directly the solution space; they do it indirectly through the exploration of the space of heuristics and/or their combinations. This space is named the associated space.
José Antonio Vázquez Rodríguez +2 more
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