Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
The State of the Art of Nurse Rostering
Journal of Scheduling
INFORMS Journal on Computing
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Hybrid heuristics for multimodal homecare scheduling
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A harmony search algorithm for nurse rostering problems
Information Sciences: an International Journal
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Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.