Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman’s perspective; this book remedies that gap. Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds. The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book’s potential with regard to real-world noisy optimization problems.

Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise. Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation. This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms. Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.

This book covers the main physical mechanisms and the different contributions (1/f noise, shot noise, etc.) behind electronic fluctuations in various spintronic devices. It presents the first comprehensive summary of fundamental noise mechanisms in both electronic and spintronic devices and is therefore unique in that aspect. The pedagogic introduction to noise is complemented by a detailed description of how one could set up a noise measurement experiment in the lab. A further extensive description of the recent progress in understanding and controlling noise in spintronics, including the boom in 2D devices, molecular spintronics, and field sensing, is accompanied by both numerous bibliography references and tens of case studies on the fundamental aspects of noise and on some important qualitative steps to understand noise in spintronics. Moreover, a detailed discussion of unsolved problems and outlook make it an essential textbook for scientists and students desiring to exploit the information hidden in noise in both spintronics and conventional electronics.

The term "neuromechanics" defines an integrative approach that combines the neuromuscular control and the biomechanical aspects of physical behavior in humans and animals. Crucial to this approach is a detailed description and modeling of the interaction between the nervous system and the controlled biomechanical plant. Only then do we have the broader context within which to understand evolution, movement mechanics, neural control, energetics, disability and rehabilitation. In addition to enabling new basic science directions, understanding the interrelations between movement neural and mechanical function should also be leveraged for the development of personalized wearable technologies to augment or restore the motor capabilities of healthy or impaired individuals. Similarly, this understanding will empower us to revisit current approaches to the design and control of robotic and humanoid systems to produce truly versatile human-like physical behavior and adaptation in real-world environments. This Research Topic is therefore poised at an opportune moment to promote understanding of apparently disparate topics into a coherent focus.

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.