This book introduces the fundamentals of Coevolutionary Computation and presents new methodologies that are developed and then employed for modern real-world problem-solving in various applications across different domains. It is structured in three main parts to support the anticipated general and frequent usage of the book. In particular, the reader is able to obtain a quick and general introduction on the principles of coevolution in Part I, and then go over in detail the specifics how coevolutionary principles are exploited and applied to solve specific problems in the relevant chapters of Parts II and III. In this manner, Part I will introduce the fundamentals in Coevolutionary Computation with no assumption made on familiarity with Evolutionary Computation literature. These fundamentals include key concepts and operational principles of both evolutionary and coevolutionary processes that are modelled as iterative algorithms and systems implementable in computing machines. Parts II and III contain various applications of coevolution to problems that are framed in the context of optimization and learning, respectively. Detailed procedural implementations are provided for those methodologies as well as analysis that highlight the improvements they bring about over conventional techniques.

New advancements to better understand coevolutionary principles including arms-race dynamics for problem-solving Showcases how coevolutionary principles are used develop new problem-solving methodologies in various domains Applications of coevolution for modern problem-solving in optimization and learning

Autorentext

Xin Yao is Tong Tin Sun Chair Professor of Machine Learning at Lingnan University, Hong Kong SAR and IEEE Fellow. His research interests include Evolutionary Computation, Ensemble Learning, and Trustworthy Artificial Intelligence. He is a recipient of 2001 IEEE Donald G. Fink Prize Paper Award, 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation and 2011 IEEE Transactions on Neural Networks Outstanding Paper Awards, 2012 Royal Society Wolfson Research Merit Award, 2013 IEEE CIS Evolutionary Computation Pioneer Award, and 2020 IEEE Frank Rosenblatt Award. He obtained his PhD in 1990 from the University of Science and Technology of China. Siang Yew Chong is Research Associate Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology, and Pengcheng Peacock Scholar under Shenzhen Municipality International Talents Center, PR China. Previously, he was with the University of Nottingham Malaysia from 2008 to 2022. He received his PhD in Computer Science from the University of Birmingham, UK, in 2007. For his work in Coevolutionary Computation, he was awarded by the IEEE Computational Intelligence Society for 2009 Outstanding PhD Dissertation and 2010 Transactions on Evolutionary Computation Outstanding Paper, and European Commision Horizon 2020 Marie-Sklodowska-Curie Actions Individual Fellowship (2016-2018).

Inhalt

Part I Coevolutionary Computation Fundamentals, Applications, and Challenges.- Chapter 1 Evolutionary Computation: An Overview.- Chapter 2 Introduction to Coevolutionary Computation.- Chapter 3 Principal Approaches of Coevolution: Competitive and Cooperative.- Chapter 4 Applications of Coevolution in Real World Problem Solving.- Chapter 5 Pathologies and Analysis in Coevolutionary Search.- Part II Coevolutionary Computation on Optimization Problems.- Chapter 6 Coevolutionary Construction of Parallel Algorithm Portfolio Optimization.- Chapter 7 Cooperative Coevolution for Large Scale Optimization.- Chapter 8 High Dimensional Optimization as Computationally Expensive Optimization.- Chapter 9 Design Space Exploration for Multimode Dataflow Mapping.- Chapter 10 Cooperative Coevolutionary Design Optimization in Concurrent Engineering.- Part III Coevolutionary Computation on Learning Problems.- Chapter 11 Coevolutionary Learning in Noncooperative Games.- Chapter 12 Generalization in Coevolutionary Learning.- Chapter 13 Impact of Problem Structures in Coevolution.