Xin Yao – författare

Evolvable hardware (EHW) refers to hardware whose architecture/structure and functions change dynamically and autonomously in order to improve its performance in carrying out tasks. The emergence of this field has been profoundly influenced by the progress in reconfigurable hardware and evolutionary computation. Traditional hardware can be inflexible – the structure and its functions are often impossible to change once it is created. However, most real world problems are not fixed – they change with time. In order to deal with these problems efficiently and effectively, different hardware structures are necessary. EHW provides an ideal approach to make hardware "soft" by adapting the structure to a problem dynamically. The contributions in this book provide the basics of reconfigurable devices so that readers will be fully prepared to understand what EHW is, why it is necessary and how it is designed. The book also discusses the leading research in digital, analog and mechanical EHW.

Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions.

This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time.

Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.

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.