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Evolutionary Multi-Task Optimization

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Evolutionary Multi-Task Optimization

Foundations and Methodologies

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

Springer
Collection: Machine Learning: Foundations, Methodologies, and Applications
Publication date : 2023-03-29

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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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About the book

Author

Liang Feng , Abhishek Gupta , Yew Soon Ong , Kay CHen Tan

Imprint

Springer

Collection

Machine Learning: Foundations, Methodologies, and Applications

Publication date

2023-03-29

Pages

219 pages

Print ISBN

9789811956492

Language

English

Ebook informations

EAN PDF

9789811956508

Price

£149.50

Copy count 2

Print count 21

File size 14213 Ko

Informations accessibility

EAN EPUB

9789811956508

Price

£149.50

Copy count 2

Print count 21

File size 28897 Ko

Informations accessibility

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About author(s)

Liang Feng

Abhishek Gupta

Yew Soon Ong

Kay CHen Tan

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