Results 121 to 130 of about 715 (165)
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Parallel cooperative optimization through hyperheuristics
Computer Aided Chemical Engineering, 2018Paola P. Oteiza +2 more
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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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A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics, 2003Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem.
Edmund K. Burke +2 more
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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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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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Applied Soft Computing, 2017
Abstract Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration ...
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Abstract Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration ...
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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
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A selection hyperheuristic guided by Thompson sampling for numerical optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021Selection hyper-heuristics have been increasingly and successfully applied to numerical and discrete optimization problems. This paper proposes HHTS, a hyper-heuristic (HH) based on the Thompson Sampling (TS) mechanism to select combinations of low-level heuristics aiming to provide solutions for various continuous single-objective optimization ...
Marcella Scoczynski Ribeiro Martins +8 more
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