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Titlebook: Heuristics for Optimization and Learning; Farouk Yalaoui,Lionel Amodeo,El-Ghazali Talbi Book 2021 The Editor(s) (if applicable) and The Au

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發(fā)表于 2025-3-21 18:45:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Heuristics for Optimization and Learning
編輯Farouk Yalaoui,Lionel Amodeo,El-Ghazali Talbi
視頻videohttp://file.papertrans.cn/427/426056/426056.mp4
概述Presents recent research on Heuristics for Optimization and Learning.Gathers contributions in optimization technique for production decision, general development for optimization and computing methods
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Heuristics for Optimization and Learning;  Farouk Yalaoui,Lionel Amodeo,El-Ghazali Talbi Book 2021 The Editor(s) (if applicable) and The Au
描述.This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces. ..The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications.?..The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields..
出版日期Book 2021
關(guān)鍵詞Optimization; Heuristics; Learning; Heuristics for Optimization and Learning; Metaheuristics
版次1
doihttps://doi.org/10.1007/978-3-030-58930-1
isbn_softcover978-3-030-58932-5
isbn_ebook978-3-030-58930-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with uristic algorithm. In order to evaluate performance of the proposed algorithm, we have carried out detailed computational experiments using well-known benchmarks from the literature. First, we present the performance of the proposed algorithm on small-sized problems; then, we show that the proposed
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Stock Market Speculation System Development Based on Technico Temporal Indicators and Data Mining T processings have been applied on our dataset before fitting the model. The experimental results of our model are compared with those obtained by: Simple Multi Linear regression that we implemented, other regression algorithms provided by the Scikit-Learn library in Python language and by RStudio in
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1860-949X st years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields..978-3-030-58932-5978-3-030-58930-1Series ISSN 1860-949X Series E-ISSN 1860-9503
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Book 2021rches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields..
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