Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I George D. Smith,Nigel C. Steele,Rudolf F. Albrecht Conference proceedin

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Luisa Martin Rojo,Concepción Gómez Esteban... Evolutionary robotics is advantageous because it gives a semi-automatic procedure to the development of a task-fulfilling control system for real robots. It is disadvantageous to some extent because of its great time consumption. Here, I will show how the time consumption can be reduced dramatic

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https://doi.org/10.1057/9781137395863of solving path planning problems is that the neural network (once trained) can be used for the same robot, with a variety of start and target positions. The genetic algorithm learns, and encodes implicitly, the calibration parameters of both the robot and the overhead camera, as well as the inverse

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Agnes M. Brazal,Kochurani Abraham used; that is, the problem has been decomposed into two subproblems: path planning and trajectory planning. This paper focuses on the second problem. The generated plans minimize the total motion time of the robots along their paths. The optimization problem is solved by evolutionary algorithms usi

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Content and Context in Theological Ethicso-one training set for digital classification problems. Each network learns a different region of the training space and all these regions fit together, like pieces of a jigsaw puzzle, to cover the entire training space. The individual networks are ‘grown’ as they are needed to form either cascades

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Agnes M. Brazal,Kochurani Abrahamropagation algorithm. Due to the proposed modular architecture the number of weight connections is less than in a fully connected multilayer perceptron. The modular network is designed to combine two different approaches of generalization known from connectionist and logical neural networks; this en

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