國立虎尾科技大學 |

Optinformatics in Evolutionary Learning and Optimization

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optinformatics in Evolutionary Learning and Optimization/ by Liang Feng, Yaqing Hou, Zexuan Zhu.
作者:	Feng, Liang.
其他作者:	Hou, Yaqing.
面頁冊數:	VIII, 144 p. 57 illus., 36 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-70920-4
ISBN:	9783030709204

Optinformatics in Evolutionary Learning and Optimization
Feng, Liang.

Optinformatics in Evolutionary Learning and Optimization[electronic resource] /by Liang Feng, Yaqing Hou, Zexuan Zhu. - 1st ed. 2021. - VIII, 144 p. 57 illus., 36 illus. in color.online resource. - Adaptation, Learning, and Optimization,251867-4542 ;. - Adaptation, Learning, and Optimization,18.

Evolutionary Learning and Optimization -- The Rise of Optinformatics in Evolutionary Computation -- Knowledge Learning and Transfer in Meta-heuristics -- Knowledge Reuse in The Form of Local Search -- Knowledge Reuse via Transfer Learning from Past Search Experiences -- Optinformatics across Heterogeneous Problem Domains and Solvers -- Potential Research Directions.

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. .

ISBN: 9783030709204

Standard No.: 10.1007/978-3-030-70920-4doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Optinformatics in Evolutionary Learning and Optimization
LDR:04150nam a22004095i 4500 001 1048780
003 DE-He213
005 20211124232301.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030709204 $9 978-3-030-70920-4
024 7 $a 10.1007/978-3-030-70920-4 $2 doi
035 $a 978-3-030-70920-4
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Feng, Liang. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352750
245 1 0 $a Optinformatics in Evolutionary Learning and Optimization $h [electronic resource] / $c by Liang Feng, Yaqing Hou, Zexuan Zhu.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a VIII, 144 p. 57 illus., 36 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Adaptation, Learning, and Optimization, $x 1867-4542 ; $v 25
505 0 $a Evolutionary Learning and Optimization -- The Rise of Optinformatics in Evolutionary Computation -- Knowledge Learning and Transfer in Meta-heuristics -- Knowledge Reuse in The Form of Local Search -- Knowledge Reuse via Transfer Learning from Past Search Experiences -- Optinformatics across Heterogeneous Problem Domains and Solvers -- Potential Research Directions.
520 $a 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. .
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Hou, Yaqing. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352751
700 1 $a Zhu, Zexuan. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352752
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030709198
776 0 8 $i Printed edition: $z 9783030709211
776 0 8 $i Printed edition: $z 9783030709228
830 0 $a Adaptation, Learning, and Optimization, $x 1867-4534 ; $v 18 $3 1261891
856 4 0 $u https://doi.org/10.1007/978-3-030-70920-4
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)