Data Driven Evolutionary Optimization
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Author |
: Yaochu Jin |
Publisher |
: Springer Nature |
Total Pages |
: 393 |
Release |
: 2021-06-28 |
ISBN-10 |
: 9783030746407 |
ISBN-13 |
: 3030746402 |
Rating |
: 4/5 (07 Downloads) |
Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin
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.
Author |
: Nirupam Chakraborti |
Publisher |
: CRC Press |
Total Pages |
: 507 |
Release |
: 2022-09-15 |
ISBN-10 |
: 9781000635867 |
ISBN-13 |
: 1000635864 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Data-Driven Evolutionary Modeling in Materials Technology by : Nirupam Chakraborti
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Author |
: Nirupam Chakraborti |
Publisher |
: CRC Press |
Total Pages |
: 319 |
Release |
: 2022-09-15 |
ISBN-10 |
: 9781000635829 |
ISBN-13 |
: 1000635821 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Data-Driven Evolutionary Modeling in Materials Technology by : Nirupam Chakraborti
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Author |
: Tero Tuovinen |
Publisher |
: Springer Nature |
Total Pages |
: 278 |
Release |
: 2021-08-19 |
ISBN-10 |
: 9783030707873 |
ISBN-13 |
: 3030707873 |
Rating |
: 4/5 (73 Downloads) |
Synopsis Computational Sciences and Artificial Intelligence in Industry by : Tero Tuovinen
This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.
Author |
: Daniel Ashlock |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 578 |
Release |
: 2006-04-04 |
ISBN-10 |
: 9780387319094 |
ISBN-13 |
: 0387319093 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Evolutionary Computation for Modeling and Optimization by : Daniel Ashlock
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
Author |
: Kenneth Price |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 544 |
Release |
: 2006-03-04 |
ISBN-10 |
: 9783540313069 |
ISBN-13 |
: 3540313060 |
Rating |
: 4/5 (69 Downloads) |
Synopsis Differential Evolution by : Kenneth Price
Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
Author |
: Liezl Stander |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2021 |
ISBN-10 |
: OCLC:1394390820 |
ISBN-13 |
: |
Rating |
: 4/5 (20 Downloads) |
Synopsis Data-driven Evolutionary Optimization for the Design Parameters of a Chemical Process by : Liezl Stander
Author |
: Wolfgang Banzhaf |
Publisher |
: Springer Nature |
Total Pages |
: 607 |
Release |
: 2020-07-08 |
ISBN-10 |
: 9783030398316 |
ISBN-13 |
: 3030398315 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Evolution in Action: Past, Present and Future by : Wolfgang Banzhaf
This edited research monograph brings together contributions from computer scientists, biologists, and engineers who are engaged with the study of evolution and how it may be applied to solve real-world problems. It also serves as a Festschrift dedicated to Erik D. Goodman, the founding director of the BEACON Center for the Study of Evolution in Action, a pioneering NSF Science and Technology Center headquartered at Michigan State University. The contributing authors are leading experts associated with the center, and they serve in top research and industrial establishments across the US and worldwide. Part I summarizes the history of the BEACON Center, with refreshingly personal chapters that describe Erik's working and leadership style, and others that discuss the development and successes of the center in the context of research funding, projects, and careers. The chapters in Part II deal with the evolution of genomes and evolvability. The contributions in Part III discuss the evolution of behavior and intelligence. Those in Part IV concentrate on the evolution of communities and collective dynamics. The chapters in Part V discuss selected evolutionary computing applications in domains such as arts and science, automated program repair, cybersecurity, mechatronics, and genomic prediction. Part VI deals with evolution in the classroom, using creativity in research, and responsible conduct in research training. The book concludes with a special chapter from Erik Goodman, a short biography that concentrates on his personal positive influences and experiences throughout his long career in academia and industry.
Author |
: Carlos A. Coello Coello |
Publisher |
: World Scientific |
Total Pages |
: 792 |
Release |
: 2004 |
ISBN-10 |
: 9789812561060 |
ISBN-13 |
: 9812561064 |
Rating |
: 4/5 (60 Downloads) |
Synopsis Applications of Multi-objective Evolutionary Algorithms by : Carlos A. Coello Coello
- Detailed MOEA applications discussed by international experts - State-of-the-art practical insights in tackling statistical optimization with MOEAs - A unique monograph covering a wide spectrum of real-world applications - Step-by-step discussion of MOEA applications in a variety of domains
Author |
: Dhruv Khandelwal |
Publisher |
: Springer Nature |
Total Pages |
: 250 |
Release |
: 2022-02-03 |
ISBN-10 |
: 9783030903435 |
ISBN-13 |
: 3030903435 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Automating Data-Driven Modelling of Dynamical Systems by : Dhruv Khandelwal
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.