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Evolutionary Multi-Task Optimization: Foundations and Methodologies - Liang Feng,Kay Chen Tan,Yew Soon Ong,Abhishek Gupta

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

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Kirjuta arvustus
inglise keel
2023-03-30
215,97 € 287,96 €

-25% koodiga BOOKS

Pehme kaanega 287,96 € Kõvakaaneline 287,96 €
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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 emula ... Täielik kirjeldus

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Lõppenud

Kirjeldus

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.

Lisateave

Autor Liang Feng, Kay Chen Tan, Yew Soon Ong, Abhishek Gupta
Kirjastaja Springer Nature Singapore
Väljalaskeaasta 2023
Kaanetüüp Kõvakaaneline
EAN 9789811956492
Kirjuta oma arvustus
Te vaatate: Evolutionary Multi-Task Optimization: Foundations and Methodologies
Teie hinnang:

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215,97 € 287,96 €