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Collective systems, abounding in nature, have evolved by natural selection to exhibit striking problem-solving capacities. Employing simple yet versatile parallel cellular models, coupled with evolutionary computation techniques, this volume explores the issue of constructing man-made systems that exhibit characteristics like those occuring in nature. Parallel cellular machines hold potential both scientifically, as vehicles for studying phenomena of interest in areas such as complex adaptive systems and artificial life, and practically, enabling the construction of novel systems, endowed with evolutionary, reproductive, regenerative, and learning capabilities. This volume examines the behavior of such machines, the complex computation they exhibit, and the application of artificial evolution to attain such systems.
This book constitutes the strictly refereed post-conference proceedings recording the scientific progress achieved at the First International Conference on Evolvable Systems: From Biology to Hardware, ICES'96, held in Tsukuba, Japan, in October 1996. The volume presents 33 revised full papers including several invited contributions surveying the state of the art in this emerging area of research and development. The volume is divided into topical sections on evolware, cellular systems, engineering applications of evolvable hardware systems, evolutionary robotics, innovative architectures, evolvable systems, evolvable hardware, and genetic programming.
This book constitutes the refereed proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware, ICES '98, held in Lausanne, Switzerland in September 1998. The 38 revised papers presented were carefully selected for inclusion in the book from numerous submissions. The papers are organized in topical sections on evaluation of digital systems, evolution of analog systems, embryonic electronics, bio-inspired systems, artifical neural networks, adaptive robotics, adaptive hardware platforms, and molecular computing.
The 14 chapters of this volume, which present an overview of new research in evolutionary dynamics, were first presented at a conference held in October 1998 at the Santa Fe Institute. The main divisions of the book are macroevolution; epochal evolution; population genetics, dynamics, and optimization; and evolution of cooperation. Individual topics include spectral landscape theory, external triggers in biological evolution, and evolutionary dynamics of asexual reproduction. Several of the contributors, like the editors, are affiliated with the Sante Fe Institute; others teach or work in physics, genetics, biology, computational neuroscience, and theoretical chemistry at universities and private institutions in the US, UK, Austria, Sweden, Australia, Israel, and Germany. Annotation copyrighted by Book News, Inc., Portland, OR
This book constitutes the refereed proceedings of the 8th International Conference on Parallel Problem Solving from Nature, PPSN 2004, held in Birmingham, UK, in September 2004. The 119 revised full papers presented were carefully reviewed and selected from 358 submissions. The papers address all current issues in biologically inspired computing; they are organized in topical sections on theoretical and foundational issues, new algorithms, applications, multi-objective optimization, co-evolution, robotics and multi-agent systems, and learning classifier systems and data mining.
Cellular automata are regular uniform networks of locally-connected finite-state machines. They are discrete systems with non-trivial behaviour. Cellular automata are ubiquitous: they are mathematical models of computation and computer models of natural systems. The book presents results of cutting edge research in cellular-automata framework of digital physics and modelling of spatially extended non-linear systems; massive-parallel computing, language acceptance, and computability; reversibility of computation, graph-theoretic analysis and logic; chaos and undecidability; evolution, learning and cryptography. The book is unique because it brings together unequalled expertise of inter-disciplinary studies at the edge of mathematics, computer science, engineering, physics and biology.
Biology has inspired electronics from the very beginning: the machines that we now call computers are deeply rooted in biological metaphors. Pioneers such as Alan Turing and John von Neumann openly declared their aim of creating arti?cial machines that could mimic some of the behaviors exhibited by natural organisms. Unfortunately, technology had not progressed enough to allow them to put their ideas into practice. The 1990s saw the introduction of programmable devices, both digital (FP- GAs) and analogue (FPAAs). These devices, by allowing the functionality and the structure of electronic devices to be easily altered, enabled researchers to endow circuits with some of the same versatility exhibited by biological entities and sparked a renaissance in the ?eld of bio-inspired electronics with the birth of what is generally known as evolvable hardware. Eversince,the?eldhasprogressedalongwiththetechnologicalimprovements and has expanded to take into account many di?erent biological processes, from evolution to learning, from development to healing. Of course, the application of these processes to electronic devices is not always straightforward (to say the least!), but rather than being discouraged, researchers in the community have shown remarkable ingenuity, as demostrated by the variety of approaches presented at this conference and included in these proceedings.
Now in its third edition the Encyclopedia of Astrobiology serves as the key to a common understanding in the extremely interdisciplinary community of astrobiologists. Each new or experienced researcher and graduate student in adjacent fields of astrobiology will appreciate this reference work in the quest to understand the big picture. The carefully selected group of active researchers contributing to this work are aiming to give a comprehensive international perspective on and to accelerate the interdisciplinary advance of astrobiology. The interdisciplinary field of astrobiology constitutes a joint arena where provocative discoveries are coalescing concerning, e.g. the prevalence of exoplanets, the diversity and hardiness of life, and its chances for emergence. Biologists, astrophysicists, (bio)-chemists, geoscientists and space scientists share this exciting mission of revealing the origin and commonality of life in the Universe. With its overview articles and its definitions the Encyclopedia of Astrobiology not only provides a common language and understanding for the members of the different disciplines but also serves for educating a new generation of young astrobiologists who are no longer separated by the jargon of individual scientific disciplines. This new edition offers ~170 new entries. More than half of the existing entries were updated, expanded or supplemented with figures supporting the understanding of the text. Especially in the fields of astrochemistry and terrestrial extremophiles but also in exoplanets and space sciences in general there is a huge body of new results that have been taken into account in this new edition. Because the entries in the Encyclopedia are in alphabetical order without regard for scientific field, this edition includes a section “Astrobiology by Discipline” which lists the entries by scientific field and subfield. This should be particularly helpful to those enquiring about astrobiology, as it illustrates the broad and detailed nature of the field.
Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.