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Titlebook: Inspired by Nature; Essays Presented to Susan Stepney,Andrew Adamatzky Book 2018 Springer International Publishing AG 2018 Evolutionary Al

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發(fā)表于 2025-3-21 18:02:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Inspired by Nature
副標(biāo)題Essays Presented to
編輯Susan Stepney,Andrew Adamatzky
視頻videohttp://file.papertrans.cn/468/467887/467887.mp4
概述Includes leading international experts’ insights into the principles of information processing and optimization in simulated and experimental living, physical and chemical substrates.Presents a tribut
叢書(shū)名稱(chēng)Emergence, Complexity and Computation
圖書(shū)封面Titlebook: Inspired by Nature; Essays Presented to Susan Stepney,Andrew Adamatzky Book 2018 Springer International Publishing AG 2018 Evolutionary Al
描述This book is a tribute to Julian Francis Miller’s ideas and achievements in computer science, evolutionary algorithms and genetic programming, electronics, unconventional computing, artificial chemistry and theoretical biology. Leading international experts in computing inspired by nature offer their insights into the principles of information processing and optimisation in simulated and experimental living, physical and chemical substrates. Miller invented Cartesian Genetic Programming (CGP) in 1999, from a representation of electronic circuits he devised with Thomson a few years earlier. The book presents a number of CGP’s wide applications, including multi-step ahead forecasting, solving artificial neural networks dogma, approximate computing, medical informatics, control engineering, evolvable hardware, and multi-objective evolutionary optimisations. The book addresses in depth the technique of ‘Evolution in Materio’, a term coined by Miller and Downing, using a range of examples of experimental prototypes of computing in disordered ensembles of graphene nanotubes, slime mould, plants, and reaction diffusion chemical systems. Advances in sub-symbolic artificial chemistries, art
出版日期Book 2018
關(guān)鍵詞Evolutionary Algorithms; Genetic Programming; Unconventional Computing; Evolution in Materio; Evolution
版次1
doihttps://doi.org/10.1007/978-3-319-67997-6
isbn_softcover978-3-319-88528-5
isbn_ebook978-3-319-67997-6Series ISSN 2194-7287 Series E-ISSN 2194-7295
issn_series 2194-7287
copyrightSpringer International Publishing AG 2018
The information of publication is updating

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發(fā)表于 2025-3-21 23:33:16 | 只看該作者
Combining Local and Global Search: A Multi-objective Evolutionary Algorithm for Cartesian Genetic Prolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6, especially when periodized with standard multi-objective genetic algorithms.
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發(fā)表于 2025-3-22 01:34:51 | 只看該作者
Approximate Computing: An Old Job for Cartesian Genetic Programming?currently one of the promising approaches used to reduce power consumption of computer systems, is a natural application for CGP. We briefly survey typical applications of CGP in approximate circuit design and outline new directions in approximate computing that could benefit from CGP.
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發(fā)表于 2025-3-22 05:33:58 | 只看該作者
Evolution in Nanomaterio: The NASCENCE Projectthe MESA+ Institute for Nanotechnology at the University of Twente using disordered networks of nanoparticles. The interested reader will also find many pointers to references that contain more details on work that has been carried out by other members of the NASCENCE consortium on composite materials based on single-walled carbon nanotubes.
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Using Reed-Muller Expansions in the Synthesis and Optimization of Boolean Quantum Circuitss it possible for the problem of synthesis and optimization of Boolean quantum circuits to be tackled within the domain of Reed-Muller logic under manufacturing constraints, for example, the interaction between qubits of the system.
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發(fā)表于 2025-3-22 19:21:28 | 只看該作者
Multi-step Ahead Forecasting Using Cartesian Genetic Programmingt results were analysed and compared using average Symmetric Mean Absolute Percentage Error (SMAPE) across all datasets. Overall, CGP achieved comparable to Support Vector Machine algorithm and performed better than Neural Networks.
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