Authors:
- Summarizes recent algorithmic advances toward realizing the notion of optinformatics in evolutionary learning and optimization
- Contains 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
- Covers future directions for algorithmic development in the field of evolutionary computation
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 25)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (5 chapters)
-
Front Matter
About this book
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.
Authors and Affiliations
-
College of Computer Science, Chongqing University, Chongqing, China
Liang Feng
-
School of Computer Science and Technology, Dalian University of Technology, Dalian, China
Yaqing Hou
-
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Zexuan Zhu
Bibliographic Information
Book Title: Optinformatics in Evolutionary Learning and Optimization
Authors: Liang Feng, Yaqing Hou, Zexuan Zhu
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-030-70920-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-70919-8Published: 30 March 2021
Softcover ISBN: 978-3-030-70922-8Published: 31 March 2022
eBook ISBN: 978-3-030-70920-4Published: 29 March 2021
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: VIII, 144
Number of Illustrations: 21 b/w illustrations, 36 illustrations in colour