Optinformatics in evolutionary learning and optimization / Liang Feng, Yaqing Hou, Zexuan Zhu.
Feng, Liang.; Hou, Yaqing.; Zhu, Zexuan.
2021
QA76.9.A43 F46 2021eb
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Title Optinformatics in evolutionary learning and optimization / Liang Feng, Yaqing Hou, Zexuan Zhu.
Author Feng, Liang.
ISBN 9783030709204 (electronic bk.)
3030709205 (electronic bk.)
3030709191
9783030709198
DOI https://doi.org/10.1007/978-3-030-70920-4
Published Cham, Switzerland : Springer, 2021.
Copyright ©2021
Language English
Description 1 online resource (viii, 144 pages)
Other Standard Identifiers 10.1007/978-3-030-70920-4 doi
Call Number QA76.9.A43 F46 2021eb
Dewey Decimal Classification 006.3/823
Summary This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics. Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 20, 2021).
Added Author Hou, Yaqing.
Zhu, Zexuan.
Series Adaptation, learning and optimization ; v. 25.
Available in Other Form Optinformatics in evolutionary learning and optimization.
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Introduction
Preliminary
Optinformatics Within a Single Problem Domain
Optinformatics Across Heterogeneous Problem Domains and Solvers
Potential Research Directions.

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