Data-Driven Evolutionary Modeling in Materials Technology

Genetic and evolutionary algorithms are used in learning and modeling, especially with big data related problems. This book presents algorithms and strategies for evolutionary multi-objective optimization of objective functions in materials science, with recent applications in primary metal production and materials design. It also discusses hybrid modeling strategy and other common modeling and simulation strategies like molecular dynamics, cellular automata, and evolutionary deep learning.

Format: Hardback
Length: 304 pages
Publication date: 15 September 2022
Publisher: Taylor & Francis Ltd

Genetic and evolutionary algorithms have gained immense popularity due to their remarkable efficacy and potential for optimization, particularly in the realm of learning and modeling, especially with the emergence of big data-related challenges. In response to this demand, this comprehensive book presents a comprehensive exploration of the algorithms and strategies specifically tailored to address the intricate issues prevalent in the field of materials science. It delves into the intricate procedures involved in evolutionary multi-objective optimization, enabling the creation of objective functions that harness the power of these algorithms. Furthermore, the book introduces readily available codes that facilitate the implementation of these optimization techniques.

Spanning a wide range of applications, from primary metal production to materials design, this book showcases recent advancements in the field. It also delves into hybrid modeling strategies, combining the strengths of evolutionary algorithms with other common modeling and simulation approaches such as molecular dynamics and cellular automata. By providing a detailed account of these strategies, the book equips readers with the knowledge and tools necessary to tackle complex materials-related problems effectively.

Key Features:

Focal Point: This book specifically focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. It provides a comprehensive framework for understanding and applying these algorithms in materials science and technology.

Algorithms and Applications: The book delves into both the algorithms and their practical applications in materials science and technology. It discusses the intricacies of evolutionary algorithms, their strengths, and limitations, and how they can be effectively integrated with other computing strategies to achieve optimal solutions.

Hybrid Data-Driven Modeling: The book highlights the importance of hybrid data-driven modeling, which couples evolutionary algorithms with generic computing strategies. This approach enables the exploration of complex systems and the optimization of multi-objective functions, paving the way for innovative materials design and development.

Thorough Coverage: The book provides a comprehensive overview of the major single and multi-objective evolutionary algorithms, their principles, and their applications in metallurgy and materials. It equips readers with a deep understanding of the underlying mechanisms and enables them to apply these algorithms to real-world problems.

Target Audience: This book is designed to cater to a diverse audience, including researchers, professionals, and graduate students in the fields of materials science, data-driven engineering, metallurgical engineering, computational materials science, structural materials, and functional materials. It offers valuable insights and practical knowledge that can enhance their research and professional endeavors.

In conclusion, this book serves as a valuable resource for anyone seeking to leverage the power of genetic and evolutionary algorithms in materials science. By presenting a comprehensive exploration of the algorithms, strategies, and applications, it empowers readers to address complex problems, innovate, and drive progress in this rapidly evolving field.

Weight: 750g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032061733