Titlebook: Automated Design of Machine Learning and Search Algorithms; Nelishia Pillay,Rong Qu Book 2021 Springer Nature Switzerland AG 2021 Machine

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Automated Design of Machine Learning and Search Algorithms978-3-030-72069-8Series ISSN 1619-7127 Series E-ISSN 2627-6461

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https://doi.org/10.1007/978-3-030-72069-8Machine Learning; Search; Algorithm Design and Analysis; AutoML; Robot Swarms; Heuristics; Deep Learning; C

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https://doi.org/10.1007/978-3-662-28676-0utomated design of algorithms and systems with less human involvement. In machine learning and meta-heuristic search algorithms, different lines of relevant research are now emerging, with findings feeding into each other. This book presents a selection of some recent advances across automated machi

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Characteristics of Mold Materialsecision variables are a set of elementary algorithm components. The combinations of these algorithm components, i.e. solutions of GCOP, thus represent different search algorithms. The objective of GCOP is to find the optimal combinations of algorithm components for solving optimisation problems. Sol

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https://doi.org/10.1007/978-3-662-28676-0orithms aim at automating the optimisation process by using a set of low-level heuristics and a machine learning mechanism to decide online which heuristic is the most appropriate one at the current stage. We mainly focus on work that establishes the performance gains that simple and sophisticated h

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Characteristics of Mold Materialsctive behaviors for robot swarms is still missing. Automatic off-line design is an appealing solution but it is prone to the so-called reality gap, which is the reason for performance drops when control software developed in simulation is deployed on real robots. We present here our research on Auto

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