Evolutionary algorithms for VLSI CAD
Evolutionary algorithms for VLSI CAD
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications
DOPTIMEL, User''s manual
The distributed multilevel ant-stigmergy algorithm used at the electric-motor design
Engineering Applications of Artificial Intelligence
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Test Pattern Generator Design Optimization Based on Genetic Algorithm
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Genetic algorithm for test pattern generator design
Applied Intelligence
Deterministic test pattern generator design
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Production scheduling with a memetic algorithm
International Journal of Innovative Computing and Applications
MatPort – online mathematics learning with a bioinspired decision-making system
International Journal of Innovative Computing and Applications
The distributed stigmergic algorithm for multi-parameter optimization
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
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This paper presents a design approach for improvement of the efficiency of a universal motor, the type of motor that is typically used in home appliances and power tools. The goal of our optimization was to find optimal values of the independent geometrical parameters of the rotor and the stator of the UM with the aim of reducing the motor's main power losses-they occur in the iron and the copper. Our procedure is based on a genetic algorithm (GA), and by using it we were able to significantly improve the motor's efficiency. The GA proved to be a simple and efficient search-and-optimization method for solving this day-to-day design problem in industry. It significantly outperformed a conventional design procedure that was used previously.