Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
HGA: a hardware-based genetic algorithm
FPGA '95 Proceedings of the 1995 ACM third international symposium on Field-programmable gate arrays
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Q-learning based on hierarchical evolutionary mechanism
WSEAS Transactions on Systems and Control
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NSP (Nurse Scheduling Problem) as a scheduling task consists of assignment of shifts and holidays to nurses for each day on the time horizon, taking into consideration a variety of conflicting interests or objectives between the hospitals and individual nurses. Many works have done for this problem using Genetic Algorithm (GA). The GA is one of the most powerful optimization methods based on the mechanics of natural evolution. However, the problem of the processing time stemming from a population-based search exists in GA. In this paper, we propose a new architecture for high-speed nurse scheduling using GA. The proposed architecture is flexible for many genetic operations on GA. Moreover, the proposed architecture realized not only the pipeline on evaluation phase, but also the pipeline on evolutionary phase on GA. Simulation results evaluating the proposed architecture are shown to the effectiveness comparison with software processing.