2021-10-072021-10-072004-06Öztürk, N. ve Öztürk, F. (2004). “Hybrid neural network and genetic algorithm based machining feature recognition”. Journal of Intelligent Manufacturing, 15(3), 287-298.0956-5515https://doi.org/10.1023/B:JIMS.0000026567.63397.d5https://link.springer.com/article/10.1023/B:JIMS.0000026567.63397.d5http://hdl.handle.net/11452/22281In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.eninfo:eu-repo/semantics/closedAccessComputer scienceEngineeringFeature recognitionNeural networksGenetic input selectionManufacturing featuresDesignClassificationSystemSearchModelBackpropagationComputational complexityComputer aided manufacturingFeature extractionGenetic algorithmsImage processingMachiningMathematical modelsParameter estimationProblem solvingComputer aided production systemsFeature recognitionGenetic input selectionNetwork modelNeural networksArticle0002212062000022-s2.0-3543131353287298153Computer science, artificial intelligenceEngineering, manufacturingComputer Aided Process Planning; Feature Recognition; Machining