Data-Driven Evolutionary Optimization

Data-Driven Evolutionary Optimization
Author: Yaochu Jin
Publisher: Springer Nature
Total Pages: 393
Release: 2021-06-28
Genre: Computers
ISBN: 3030746402

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.

Multi-Objective Optimization using Evolutionary Algorithms

Multi-Objective Optimization using Evolutionary Algorithms
Author: Kalyanmoy Deb
Publisher: John Wiley & Sons
Total Pages: 540
Release: 2001-07-05
Genre: Mathematics
ISBN: 9780471873396

Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.

Innovization

Innovization
Author: Kalyanmoy Deb
Publisher: Springer
Total Pages: 300
Release: 2016-06-12
Genre: Computers
ISBN: 9783540731726

Every designer wants to know what makes a product or process optimal. This book suggests a holistic approach to optimization that involves two steps: find a set of trade-off optimal solutions involving two or more conflicting objectives related to the problem, and then analyze these high-performing solutions to determine solution principles that commonly prevail among these solutions. Since the solutions are optimal, such common principles are likely to exist; and since these principles are common to many solutions they are likely to provide robust, reliable solution principles. The author is one of the leading researchers in multiobjective optimization, and an expert in design methodology. In this book he offers introductions to innovation in design; multiobjective optimization, in particular evolutionary multiobjective optimization (EMO) techniques that find multiple, trade-off, optimal solutions; and knowledge extraction from multivariate data using graphical, regression and clustering techniques. He then introduces his innovization methodology for revealing new, innovative design principles related to decision variables and objectives, and he demonstrates it through engineering case studies, in particular product and process design problems. The book will be of benefit to practitioners, researchers and students engaged with issues of optimal design, in particular in domains such as engineering design, product design, engineering optimization, manufacturing, process design and complex systems. The sample computer code referenced is available from the author's website.

Multi-Objective Optimization using Artificial Intelligence Techniques

Multi-Objective Optimization using Artificial Intelligence Techniques
Author: Seyedali Mirjalili
Publisher: Springer
Total Pages: 66
Release: 2019-07-24
Genre: Technology & Engineering
ISBN: 3030248356

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Multiobjective Optimization

Multiobjective Optimization
Author: Jürgen Branke
Publisher: Springer
Total Pages: 481
Release: 2008-10-18
Genre: Computers
ISBN: 3540889086

Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support. This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA). This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems
Author: Carlos Coello Coello
Publisher: Springer Science & Business Media
Total Pages: 810
Release: 2007-08-26
Genre: Computers
ISBN: 0387367977

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

Evolutionary Multiobjective Optimization

Evolutionary Multiobjective Optimization
Author: Ajith Abraham
Publisher: Springer Science & Business Media
Total Pages: 313
Release: 2005-09-05
Genre: Computers
ISBN: 1846281377

Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

Approximation and Optimization

Approximation and Optimization
Author: Ioannis C. Demetriou
Publisher: Springer
Total Pages: 244
Release: 2019-05-10
Genre: Mathematics
ISBN: 3030127672

This book focuses on the development of approximation-related algorithms and their relevant applications. Individual contributions are written by leading experts and reflect emerging directions and connections in data approximation and optimization. Chapters discuss state of the art topics with highly relevant applications throughout science, engineering, technology and social sciences. Academics, researchers, data science practitioners, business analysts, social sciences investigators and graduate students will find the number of illustrations, applications, and examples provided useful. This volume is based on the conference Approximation and Optimization: Algorithms, Complexity, and Applications, which was held in the National and Kapodistrian University of Athens, Greece, June 29–30, 2017. The mix of survey and research content includes topics in approximations to discrete noisy data; binary sequences; design of networks and energy systems; fuzzy control; large scale optimization; noisy data; data-dependent approximation; networked control systems; machine learning ; optimal design; no free lunch theorem; non-linearly constrained optimization; spectroscopy.

Evolutionary Multi-Criterion Optimization

Evolutionary Multi-Criterion Optimization
Author: Hisao Ishibuchi
Publisher: Springer Nature
Total Pages: 781
Release: 2021-03-24
Genre: Computers
ISBN: 3030720624

This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.