Titlebook: Genetic Programming; 22nd European Confer Lukas Sekanina,Ting Hu,Pablo García-Sánchez Conference proceedings 2019 Springer Nature Switzerla

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https://doi.org/10.1007/978-3-662-26494-2al industrial processes. Since various types of mechanical and electrical faults could occur, induction motor fault diagnosis can be interpreted as a multi-label classification problem. The current and vibration input data collected by monitoring a motor often require signal processing to extract fe

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Falk Würfele,Alexander Muchowskiwhere we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of su

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https://doi.org/10.1007/978-3-663-14479-3popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a . representation where each observation corresponds to a collection on a subject at a

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,Perspektive Hoffnung für das Dorf, data by utilizing a pseudo-randomly generated binary sequence. Generating a cryptographically secure sequence is not an easy task and requires a Boolean function possessing multiple cryptographic properties. One of the most successful ways of designing these functions is genetic programming. In thi

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https://doi.org/10.1007/978-3-531-90581-5 allowed by Parseval’s relation. Hyper-bent functions, in turn, are those bent functions which additionally reach maximum distance from all bijective monomial functions, and provide further security towards approximation attacks. Being characterized by a stricter definition, hyper-bent functions are

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https://doi.org/10.1007/978-3-8349-9508-7Using 512 bit Advanced Vector Extensions, previous development history and Intel documentation, BNF grammar based genetic improvement automatically ports RNAfold to AVX, giving up to a 1.77 fold speed up. The evolved code pull request is an accepted GI software maintenance update to bioinformatics package ..

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