Data Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science Yaochu Jin, Handing Wang, Chaoli Sun. I started working on fitness approximation in evolutionary optimization when I moved back to Germany from the USA in 1999 to ...

Data Driven 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.

More Books:

Evolutionary Optimization
Language: en
Pages: 418
Authors: Ruhul Sarker, Masoud Mohammadian, Xin Yao
Categories: Business & Economics
Type: BOOK - Published: 2006-04-11 - Publisher: Springer Science & Business Media

Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization
Data-Driven Evolutionary Optimization
Language: en
Pages: 393
Authors: Yaochu Jin, Handing Wang, Chaoli Sun
Categories: Computers
Type: BOOK - Published: 2021-06-28 - Publisher: Springer Nature

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
Evolutionary Optimization Algorithms
Language: en
Pages: 273
Authors: Altaf Q. H. Badar
Categories: Technology & Engineering
Type: BOOK - Published: 2021-10-30 - Publisher: CRC Press

This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems. The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and
Constraint-Handling in Evolutionary Optimization
Language: en
Pages: 264
Authors: Efrén Mezura-Montes
Categories: Computers
Type: BOOK - Published: 2009-04-07 - Publisher: Springer Science & Business Media

This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.
Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design
Language: en
Pages: 305
Authors: Jacques Periaux, Felipe Gonzalez, Dong Seop Chris Lee
Categories: Technology & Engineering
Type: BOOK - Published: 2015-04-13 - Publisher: Springer

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization