Hyper-heuristic
Fault tolerant based hyper heuristic algorithm for task scheduling in cloud
... low-level heuristic is to be used for finding enhanced solutions in task ...Based Hyper Heuristic Algorithm (FT-HHA) provides fault tolerance in task scheduling by using task replication and task ...
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New hyper-heuristic algorithm for gene fragment assembly
... new hyper-heuristic algorithm is designed and developed in terms of the computational ...the heuristic operations of CRO and QIEA to increase the search ability in order to find a good quality of ...
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Fuzzy adaptive parameter control of a late acceptance hyper heuristic
... selection hyper-heuristic combining a learning heuristic selection method with late ...The heuristic selection method, referred to as RUA1-F1FPS is based on objective value (fitness) ...
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Fuzzy adaptive parameter control of a late acceptance hyper heuristic
... the hyper-heuristic is split into influences the number of times the fuzzy system is ...the hyper-heuristic may have already prematurely converged causing sub-optimal solutions to be found ...
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A case study of controlling crossover in a selection hyper heuristic framework using the multidimensional knapsack problem
... performing hyper-heuristic with crossover controlled at the domain-specific level is tested by applying it to two further bench- mark sets, each with differing ...the hyper-heuristic in terms ...
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Grammatical evolution hyper heuristic for combinatorial optimization problems
... evolution hyper-heuristic with an adaptive memory (GE-HH) and the grammatical evolution hyper-heuristic without an adaptive memory (GE- HH*) using the same parameter values and computational ...
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A multi objective hyper heuristic based on choice function
... objective hyper-heuristics, a decision maker could be a selection method that decides which is the best low level heuristics to select at each decision ...multi-objective hyper-heuristic frame- work. ...
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A genetic programming hyper heuristic for the multidimensional knapsack problem
... of hyper- heuristics; heuristic selection methodologies and heuristic generation ...methodologies. Heuristic selection methodologies select a low-level heuristic to apply at a given ...
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An advanced IWD based Hyper heuristic workflow scheduling in computational grid
... with hyper-heuristic search for achieving better performance optimization for workflow distribution in computational grid ...A hyper heuristic search is performed which undergoes multiple ...
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A Parallel Hyper-heuristic Approach for the Two-dimensional Rectangular Strip-packing Problem
... In this paper, we present a parallel hyper-heuristic approach for two-dimensional rectangular strip-packing problems (2DSP). This is an island model with a special master-slave structure, and in all the ...
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Dynamic scheduling of multi product continuous biopharmaceutical facilities : a hyper heuristic framework
... selection hyper-heuristic as the search space is on potential rules and not direct solutions to the ...a hyper-heuristic as there is a global search for control ...policies. ...
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A new model and a hyper heuristic approach for two dimensional shelf space allocation
... – Most of the shelf space allocation models discussed in the literature treat shelf space one-dimensionally, quantified either in volume or in shelf length. That is, the capacity of shelf is usually quantified as a single ...
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A Tabu Search hyper heuristic strategy for t way test suite generation
... Level Hyper-Heuristic ...existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable ...
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Empirical study of Hyper Heuristic on severely limited noisy optimization function
... introduced Hyper-heuristics in 2000 and he further extends the study of hyper-heuristics in scheduling problems (Cowling and Chakhlevith, ...of hyper-heuristics usage (Burke et al , 2013) and in his ...
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A hyper heuristic approach to automated generation of mutation operators for evolutionary programming
... In this paper we have used genetic programming (GP) as an offline hyper-heuristic to automat- ically evolve probability distributions, to use as mutation operators in evolutionary programming (EP). This is ...
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Improving performance of a hyper heuristic using a multilayer perceptron for vehicle routing
... the heuristic selection machine is not ...the heuristic selection machine is choosing randomly among all ...the heuristic selection mechanism is nearly random and it chooses from five particular ...
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A modified choice function hyper heuristic controlling unary and binary operators
... each hyper-heuristic on each ...performing hyper-heuristic for each instance awarded 10 points, the second 8 points and then each further hyper-heuristic is awarded 6, 5, 4, 3, ...
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An apprenticeship learning hyper heuristic for vehicle routing in HyFlex
... ation hyper-heuristics ...a heuristic or component of a solution method include job shop scheduling [19], 0/1 knapsack [20] and strip packing ...selection hyper-heuristic components for cross ...
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A learning automata based multiobjective hyper heuristic
... On the other hand, there are a few studies on multiobjective search methods that make use of multiple MOEAs. [44] proposed a multialgorithm genetically adaptive multiobjective (AMALGAM) method performing cooperative ...
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A tensor based hyper heuristic for nurse rostering
... In this paper, following (Asta and ¨ Ozcan, 2015), CP decomposition method is used for factorization. While Tucker decomposition is a generalization of the CP method, there are few advantages based on which the latter ...
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