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[null,"1.","Record Nr.",null,"UNINA9910488717903321"]
[null,null,"Autore",null,"Jin Yaochu"]
[null,null,"Titolo",null,"Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science / / by Yaochu Jin, Handing Wang, Chaoli Sun"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"9783030746407"]
[{"$nulls":4},"3030746402"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (408 pages)"]
[null,null,"Collana",null,"Studies in Computational Intelligence, , 1860-9503 ; ; 975"]
[null,null,"Disciplina",null,"620.00285"]
[null,null,"Soggetti",null,"Engineering - Data processing"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Data Engineering"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction to Optimization -- Classical Optimization Algorithms -- Evolutionary and Swarm Optimization -- Introduction to Machine Learning -- Data-Driven Surrogate-Assisted Evolutionary Optimization -- Multi-Surrogate-Assisted Single-Objective Optimization -- Surrogate-Assisted Multi-Objective Evolutionary Optimization."]
[null,null,null,"Sommario/riassunto","Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization "]
[{"$nulls":4},"ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included."]