The classical theory of natural selection, as developed by Fisher, Haldane, and 'Wright, and their followers, is in a sense a statistical theory. By and large the classical theory assumes that the underlying environment in which evolution transpires is both constant and stable - the theory is in this sense deterministic. In reality, on the other hand, nature is almost always changing and unstable. We do not yet possess a complete theory of natural selection in stochastic environ ments. Perhaps it has been thought that such a theory is unimportant, or that it would be too difficult. Our own view is that the time is now ripe for the development of a probabilistic theory of natural selection. The present volume is an attempt to provide an elementary introduction to this probabilistic theory. Each author was asked to con tribute a simple, basic introduction to his or her specialty, including lively discussions and speculation. We hope that the book contributes further to the understanding of the roles of "Chance and Necessity" (Monod 1971) as integrated components of adaptation in nature.

Most organisms and populations have to cope with hostile environments, threatening their existence. Their ability to respond phenotypically and genetically to these challenges and to evolve adaptive mechanisms is, therefore, crucial. The contributions to this book aim at understanding, from a evolutionary perspective, the impact of stress on biological systems. Scientists, applying different approaches spanning from the molecular and the protein level to individuals, populations and ecosystems, explore how organisms adapt to extreme environments, how stress changes genetic structure and affects life histories, how organisms cope with thermal stress through acclimation, and how environmental and genetic stress induce fluctuating asymmetry, shape selection pressure and cause extinction of populations. Finally, it discusses the role of stress in evolutionary change, from stress induced mutations and selection to speciation and evolution at the geological time scale. The book contains reviews and novel scientific results on the subject. It will be of interest to both researchers and graduate students and may serve as a text for graduate courses.

The Adaptive Markets Hypothesis (AMH) presents a formal and systematic exposition of a new narrative about financial markets that reconciles rational investor behaviour with periods of temporary financial insanity. In this narrative, intelligent but fallible investors learn from and adapt to randomly shifting environments. Financial markets may not always be efficient, but they are highly competitive, innovative, and adaptive, varying in their degree of efficiency as investor populations and the financial landscape change over time. Andrew Lo and Ruixun Zhang develop the mathematical foundations of the AMH—a simple yet surprisingly powerful set of evolutionary models of behaviour—and then apply these foundations to show how the most fundamental economic behaviours that we take for granted can arise solely through natural selection. Drawing on recent advances in cognitive neuroscience and artificial intelligence, the book also explores how our brain affects economic and financial decision making. The AMH can be applied in many contexts, ranging from designing trading strategies, to managing risk and understanding financial crises, to formulating macroprudential policies to promote financial stability. This volume is a must read for anyone who has ever been puzzled and concerned by the behaviour of financial markets and the implications for their personal wealth, and seeks to learn how best to respond to such behaviour.

Adaptive Control (second edition) shows how a desired level of system performance can be maintained automatically and in real time, even when process or disturbance parameters are unknown and variable. It is a coherent exposition of the many aspects of this field, setting out the problems to be addressed and moving on to solutions, their practical significance and their application. Discrete-time aspects of adaptive control are emphasized to reflect the importance of digital computers in the application of the ideas presented. The second edition is thoroughly revised to throw light on recent developments in theory and applications with new chapters on: multimodel adaptive control with switching, direct and indirect adaptive regulation and adaptive feedforward disturbance compensation. Many algorithms are newly presented in MATLAB® m-file format to facilitate their employment in real systems. Classroom-tested slides for instructors to use in teaching this material are also now provided. All of this supplementary electronic material can be downloaded from fill in URL. The core material is also up-dated and re-edited to keep its perspective in line with modern ideas and more closely to associate algorithms with their applications giving the reader a solid grounding in: synthesis and analysis of parameter adaptation algorithms, recursive plant model identification in open and closed loop, robust digital control for adaptive control; • robust parameter adaptation algorithms, practical considerations and applications, including flexible transmission systems, active vibration control and broadband disturbance rejection and a supplementary introduction on hot dip galvanizing and a phosphate drying furnace. Control researchers and applied mathematicians will find Adaptive Control of significant and enduring interest and its use of example and application will appeal to practitioners working with unknown- and variable-parameter plant. Praise for the first edition: ...well written, interesting and easy to follow, so that it constitutes a valuable addition to the monographies in adaptive control for discrete-time linear systems... suitable (at least in part) for use in graduate courses in adaptive control.

Adaptive Control provides techniques for automatic, real-time adjustments in controller parameters with a view to achieving and/or maintaining a desirable level of system performance in the presence of unknown or variable process parameters. Many aspects of the field are dealt with in coherent and orderly fashion, starting with the problems posed by system uncertainties and moving on to the presentation of solutions and their practical significance. Within the general context of recent developments, the book looks at: • synthesis and analysis of parameter adaptation algorithms; • recursive plant-model identification in open and closed loop; • robust digital control for adaptive control; • direct and indirect adaptive control; and • practical aspects and applications. To reflect the importance of digital computers for the application of adaptive control techniques, discrete-time aspects are emphasized. To guide the reader, the book contains various applications of adaptive control techniques.

The impacts of human-induced climate change are largely mediated by water, such as alterations in precipitation and glacial melt patterns, variations in river flow, increased occurrence of droughts and floods, and sea level rise in densely populated coastal areas. Such phenomena impact both urban and rural communities in developed, emerging, and developing countries. Taking a systems approach, this book analyzes evidence from 26 countries and identifies common barriers and bridges for local adaptation to climate change through water resources management. It includes a global set of case studies from places experiencing increased environmental and social pressure due to population growth, development and migration, including in Africa, Asia, Australia, Europe, North and South America. All chapters consider the crosscutting themes of adaptive capacity, equity, and sustainability. These point to resilient water allocation policies and practices that are capable of protecting social and environmental interests, whilst ensuring the efficient use of an often-scarce resource.

Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.

In the context of this book, adaptation is taken to mean a feature of a system aimed at achieving the best possible performance, when mathematical models of the environment and the system itself are not fully available. This has applications ranging from theories of visual perception and the processing of information, to the more technical problems of friction compensation and adaptive classification of signals in fixed-weight recurrent neural networks. Largely devoted to the problems of adaptive regulation, tracking and identification, this book presents a unifying system-theoretic view on the problem of adaptation in dynamical systems. Special attention is given to systems with nonlinearly parameterized models of uncertainty. Concepts, methods and algorithms given in the text can be successfully employed in wider areas of science and technology. The detailed examples and background information make this book suitable for a wide range of researchers and graduates in cybernetics, mathematical modelling and neuroscience.

This book is a collection of best selected papers presented at the International Conference on Inventive Computation and Information Technologies (ICICIT 2021), organized during 12–13 August 2021. The book includes papers in the research area of information sciences and communication engineering. The book presents novel and innovative research results in theory, methodology and applications of communication engineering and information technologies.

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.