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001435430 020__ $$a3030709205$$q(electronic bk.)
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001435430 0247_ $$a10.1007/978-3-030-70920-4$$2doi
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001435430 050_4 $$aQA76.9.A43$$bF46 2021eb
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001435430 1001_ $$aFeng, Liang.
001435430 24510 $$aOptinformatics in evolutionary learning and optimization /$$cLiang Feng, Yaqing Hou, Zexuan Zhu.
001435430 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001435430 264_4 $$c©2021
001435430 300__ $$a1 online resource (viii, 144 pages)
001435430 336__ $$atext$$btxt$$2rdacontent
001435430 337__ $$acomputer$$bc$$2rdamedia
001435430 338__ $$aonline resource$$bcr$$2rdacarrier
001435430 4901_ $$aAdaptation, Learning, and Optimization ;$$vv. 25
001435430 504__ $$aIncludes bibliographical references.
001435430 50500 $$tIntroduction --$$tPreliminary --$$tOptinformatics Within a Single Problem Domain --$$tOptinformatics Across Heterogeneous Problem Domains and Solvers --$$tPotential Research Directions.
001435430 506__ $$aAccess limited to authorized users.
001435430 520__ $$aThis 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.
001435430 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 20, 2021).
001435430 650_0 $$aComputer algorithms.
001435430 650_0 $$aEvolutionary computation.
001435430 650_0 $$aMachine learning.
001435430 650_6 $$aAlgorithmes.
001435430 650_6 $$aRéseaux neuronaux à structure évolutive.
001435430 650_6 $$aApprentissage automatique.
001435430 655_0 $$aElectronic books.
001435430 7001_ $$aHou, Yaqing.
001435430 7001_ $$aZhu, Zexuan.
001435430 77608 $$iPrint version:$$aFeng, Liang.$$tOptinformatics in evolutionary learning and optimization.$$dCham, Switzerland : Springer, 2021$$z3030709191$$z9783030709198$$w(OCoLC)1237347614
001435430 830_0 $$aAdaptation, learning and optimization ;$$vv. 25.
001435430 852__ $$bebk
001435430 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-70920-4$$zOnline Access$$91397441.1
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001435430 980__ $$aBIB
001435430 980__ $$aEBOOK
001435430 982__ $$aEbook
001435430 983__ $$aOnline
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