Results 151 to 160 of about 1,136 (193)
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Subheuristic search and scalability in a hyperheuristic
Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008Our previous work has introduced a {hyperheuristic} (HH) approach based on Genetic Programming (GP). There, GP employs user-given languages where domain-specific local heuristics are used as primitives for producing specialised metaheuristics (MH).
Robert E. Keller, Riccardo Poli
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Stable solving of CVRPs using hyperheuristics
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009In this paper we present a hill-climbing based hyperheuristic which is able to solve instances of the capacitated vehicle routing problem. The hyperheuristic manages a sequence of constructive-perturbative pairs of low-level heuristics which are applied sequentially in order to construct and improve partial solutions.
Pablo Garrido 0001, Carlos Castro 0001
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An ant colony hyperheuristic approach for matrix bandwidth reduction
Applied Soft Computing Journal, 2020Abstract This paper considers the bandwidth reduction problem for large-scale matrices in serial computations. A heuristic for bandwidth reduction reorders the rows and columns of a given sparse matrix so that the method places entries with a nonzero value as close to the main diagonal as possible.
Sanderson L Gonzaga de Oliveira
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Towards a distributed hyperheuristic deploy architecture
Proceedings of the 7th Euro American Conference on Telematics and Information Systems, 2014The hyperheuristic term is known in the optimization field as an automated methodology for selecting or generating heuristics to solve hard computational search problems. From the design perspective, it is based on decoupling the solving intelligence from the domain expertise, allowing to reuse the same solver for multiple, usually related problem ...
Enrique Urra +2 more
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Hyperheuristics Based on Parametrized Metaheuristic Schemes
Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015The use of a unified parametrized scheme for metaheuristics facilitates the development of metaheuristics and their application. The unified scheme can also be used to implement hyperheuristics on top of parametrized metaheuristics, selecting appropriate values for the metaheuristic parameters, and consequently the metaheuristic itself.
José-Matías Cutillas-Lozano +2 more
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Concrete hyperheuristic framework for test case prioritization
Journal of Software: Evolution and Process, 2018AbstractTest case prioritization (TCP), which aims to find the optimal test case execution sequences for specific testing objects, has been widely used in regression testing. A wide variety of search methodologies and algorithms have been proposed to optimize test case execution sequences, namely, search‐based TCP. However, different algorithms perform
Yi Bian, Zheng Li, Junxia Guo
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Self-adaptive hyperheuristic and greedy search
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008In previous work, we have introduced an effective and resource-efficient hyperheuristic that uses Genetic Programming as its search heuristic on the space of heuristics. Here, we show that the hyperheuristic performs better than purely greedy and even only mostly greedy flavours of hill climbing.
Robert E. Keller, Riccardo Poli
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Evolutionary hyperheuristic for capacitated vehicle routing problem
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013In this paper a novel constructive hyperheuristic for CVRP is proposed. This hyperheuristic, called HyperPOEMS, is based on an evolutionary-based iterative local search algorithm. Its inherent characteristics make it capable of autonomously searching a structured space of low-level domain specific heuristics for their suitable combinations that produce
Jaromír Mlejnek, Jirí Kubalík
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Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008
This work presents a new parallel model for the solution of multi-objective optimization problems. The model combines a parallel island-based scheme with a hyperheuristic approach in order to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one
Coromoto León +2 more
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This work presents a new parallel model for the solution of multi-objective optimization problems. The model combines a parallel island-based scheme with a hyperheuristic approach in order to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one
Coromoto León +2 more
openaire +1 more source