Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science
Jin, Yaochu 1966-
Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science by Yaochu Jin, Handing Wang, Chaoli Sun. - First edition 2021 - 1 recurso en línea (XXV, 393 páginas) 159 ilustraciones, 76 ilustraciones a color - Studies in Computational Intelligence 975 1860-9503 Intelligent Technologies and Robotics (SpringerNature-42732) Intelligent Technologies and Robotics (R0) (SpringerNature-43728) .
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.
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 ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
9783030746407
10.1007/978-3-030-74640-7 doi
Optimización matemática
QA402.5 / 2021 EB
Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science by Yaochu Jin, Handing Wang, Chaoli Sun. - First edition 2021 - 1 recurso en línea (XXV, 393 páginas) 159 ilustraciones, 76 ilustraciones a color - Studies in Computational Intelligence 975 1860-9503 Intelligent Technologies and Robotics (SpringerNature-42732) Intelligent Technologies and Robotics (R0) (SpringerNature-43728) .
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.
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 ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
9783030746407
10.1007/978-3-030-74640-7 doi
Optimización matemática
QA402.5 / 2021 EB