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Evolutionary multi-task optimizationfoundations and methodologies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evolutionary multi-task optimizationby Liang Feng ... [et al.].
其他題名:	foundations and methodologies /
其他作者:	Feng, Liang.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	x, 219 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Evolutionary computation.
電子資源:	https://doi.org/10.1007/978-981-19-5650-8
ISBN:	9789811956508$q(electronic bk.)

Evolutionary multi-task optimizationfoundations and methodologies /
Evolutionary multi-task optimizationfoundations and methodologies /[electronic resource] :by Liang Feng ... [et al.]. - Singapore :Springer Nature Singapore :2023. - x, 219 p. :ill., digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

Chapter 1.Introduction -- Chapter 2. Overview and Application-driven Motivations of Evolutionary Multitasking -- Chapter 3.The Multi-factorial Evolutionary Algorithm -- Chapter 4. Multi-factorial Evolutionary Algorithm with Adaptive Knowledge Transfer -- Chapter 5.Explicit Evolutionary Multi-task Optimization Algorithm -- Chapter 6.Evolutionary Multi-task Optimization for Generalized Vehicle Routing Problem With Occasional Drivers -- Chapter 7. Explicit Evolutionary Multi-task Optimization for Capacitated Vehicle Routing Problem -- Chapter 8. Multi-Space Evolutionary Search for Large Scale Single-Objective Optimization -- Chapter 9.Multi-Space Evolutionary Search for Large-scale Multi-Objective Optimization.

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.

ISBN: 9789811956508$q(electronic bk.)

Standard No.: 10.1007/978-981-19-5650-8doiSubjects--Topical Terms:

231709
Evolutionary computation.

LC Class. No.: QA76.618

Dewey Class. No.: 006.3823

Evolutionary multi-task optimizationfoundations and methodologies /
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520 $a 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.
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