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Titlebook: Algorithm Portfolios; Advances, Applicatio Dimitris Souravlias,Konstantinos E. Parsopoulos,Pa Book 2021 The Author(s), under exclusive lice

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發(fā)表于 2025-3-21 17:11:55 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Algorithm Portfolios
期刊簡(jiǎn)稱(chēng)Advances, Applicatio
影響因子2023Dimitris Souravlias,Konstantinos E. Parsopoulos,Pa
視頻videohttp://file.papertrans.cn/153/152829/152829.mp4
發(fā)行地址Primary reference point for researchers and doctoral students seeking a quick guide.Provides essential insights about choices of algorithms and configurations to tackle optimization problems.Explores
學(xué)科分類(lèi)SpringerBriefs in Optimization
圖書(shū)封面Titlebook: Algorithm Portfolios; Advances, Applicatio Dimitris Souravlias,Konstantinos E. Parsopoulos,Pa Book 2021 The Author(s), under exclusive lice
影響因子.This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, andopen problems in the design of algorithm portfolios and applications are explored to further motivate research in this field..
Pindex Book 2021
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書(shū)目名稱(chēng)Algorithm Portfolios影響因子(影響力)




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Selection of Constituent Algorithms,ithms in solving hard optimization problems. The concurrent or interchangeable application of all the constituent algorithms of the portfolio aims at exploiting their individual advantages and suppressing their weaknesses through complementarity. Despite the inherent tolerance of algorithm portfolio
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Sequential and Parallel Models,ingle processing unit, consuming fixed fractions of their allocated computation resources at each turn. The parallel case harnesses the computational efficiency of multiple processing units, as presented in the resource allocation models of the previous chapter. Each constituent algorithm runs eithe
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Sequential and Parallel Models, portion of the total amount of resources provided to the portfolio. The present chapter reviews state-of-the-art portfolio frameworks for each model type, along with their distinctive characteristics and algorithmic peculiarities.
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