Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevan. Bestandsnummer des Verk�ufers 1488610220

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Evolutionary Multi-Task Optimization

Liang Feng

Verlag: Springer Nature Singapore Apr 2025, 2025

ISBN 10: 9811956529 ISBN 13: 9789811956522

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date.Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness. 219 pp. Englisch. Bestandsnummer des Verk�ufers 9789811956522

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Evolutionary Multi-task Optimization : Foundations and Methodologies

Feng, Liang; Gupta, Abhishek; Tan, Kay Chen; Ong, Yew Soon

Verlag: Springer, 2025

ISBN 10: 9811956529 ISBN 13: 9789811956522

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Evolutionary Multi-task Optimization : Foundations and Methodologies

Feng, Liang; Gupta, Abhishek; Tan, Kay Chen; Ong, Yew Soon

Verlag: Springer, 2025

ISBN 10: 9811956529 ISBN 13: 9789811956522

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Evolutionary Multi-Task Optimization : Foundations and Methodologies

Liang Feng

Verlag: Springer Nature Singapore, Springer Nature Singapore, 2025

ISBN 10: 9811956529 ISBN 13: 9789811956522

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date.Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness. Bestandsnummer des Verk�ufers 9789811956522

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