Memetic Computation: The Mainspring of Knowledge Transfer in a Data-Driven Optim

Description: Memetic Computation by Abhishek Gupta, Yew-Soon Ong This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC – beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solvingprowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly – thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential. Back Cover This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC - beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly - thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential. Table of Contents Introduction: Rise of Memetics in Computing.- Canonical Memetic Algorithms.- Data-Driven Adaptation in Memetic Algorithms.- The Memetic Automaton.- Sequential Knowledge Transfer across Problems.- Multitask Knowledge Transfer across Problems.- Future Direction: Meme Space Evolutions. Feature Presents a data-driven view of optimization through the framework of memetic computation (MC) Provides the first comprehensive coverage of memetic computation Includes a summary of the complete timeline of MC research activities Explores newly emerging problem settings from the optimization literature in a theoretical manner and systematically describes the associated algorithmic developments that align with the general theme of memetics Offers novel theories and algorithms for principled transfer and multitask optimization Introduces the novel idea of meme-based search space compression for large-scale optimization Details ISBN3030027287 Author Yew-Soon Ong Year 2019 ISBN-10 3030027287 ISBN-13 9783030027285 Format Hardcover Pages 104 Publication Date 2019-02-05 Short Title Memetic Computation Language English DOI 10.1007/978-3-030-02729-2 Series Number 21 Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Illustrations XI, 104 p. Publisher Springer Nature Switzerland AG Edition Description 1st ed. 2019 Series Adaptation, Learning, and Optimization Subtitle The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era DEWEY 006.3 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:126673772;

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