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Handbook of Evolutionary Computation by Thomas Baeck,D.B Fogel,Z Michalewicz Pdf
Many scientists and engineers now use the paradigms of evolutionary computation (genetic algorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies. The Handbook of Evolutionary Computation addresses the need for a comprehensive source of reference in the maturing field of evolutionary computation. The handbook is available in a looseleaf print format and an online format.
Evolutionary Computation 1 by Thomas Baeck,D.B Fogel,Z Michalewicz Pdf
The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.
Introduction to Evolutionary Computing by Agoston E. Eiben,J.E. Smith Pdf
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Author : Juan J. Romero Publisher : Springer Science & Business Media Page : 465 pages File Size : 43,5 Mb Release : 2008 Category : Art ISBN : 9783540728764
The Art of Artificial Evolution by Juan J. Romero Pdf
Art is the Queen of all sciences communicating knowledge to all the generations of the world. Leonardo da Vinci Artistic behavior is one of the most valued qualities of the human mind. Although artistic manifestations vary from culture to culture, dedication to artistic tasks is common to all. In other words, artistic behavior is a universal trait of the human species. The current, Western de?nition of art is relatively new. However, a d- ication to artistic endeavors — such as the embellishment of tools, body - namentation, or gathering of unusual, arguably aesthetic, objects — can be traced back to the origins of humanity. That is, art is ever-present in human history and prehistory. Artandsciencesharealongandenduringrelationship.Thebest-known- ample of the explorationof this relationship is probably the work of Leonardo da Vinci. Somewhere in the 19th century art and science grew apart, but the cross-transfer of concepts between the two domains continued to exist. Currently, albeit the need for specialization, there is a growing interest in the exploration of the connections between art and science. Focusingoncomputerscience,itisinterestingtonoticethatearlypioneers of this discipline such as Ada Byron and Alan Turing showed an interest in using computational devices for art-making purposes. Oddly, in spite of this early interest and the ubiquity of art, it has received relatively little attention fromthe computersciencecommunityingeneral,and,moresurprisingly,from the arti?cial intelligence community.
Evolutionary Computation by D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu Pdf
Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.
Handbook of Research on Natural Computing for Optimization Problems by Mandal, Jyotsna Kumar Pdf
Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.
Handbook of Evolutionary Machine Learning by Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang Pdf
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.
Evolutionary Computation by Kenneth A. De Jong Pdf
This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
Handbook of Genetic Programming Applications by Amir H. Gandomi,Amir H. Alavi,Conor Ryan Pdf
This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
Handbook of Neuroevolution Through Erlang by Gene I. Sher Pdf
Handbook of Neuroevolution Through Erlang presents both the theory behind, and the methodology of, developing a neuroevolutionary-based computational intelligence system using Erlang. With a foreword written by Joe Armstrong, this handbook offers an extensive tutorial for creating a state of the art Topology and Weight Evolving Artificial Neural Network (TWEANN) platform. In a step-by-step format, the reader is guided from a single simulated neuron to a complete system. By following these steps, the reader will be able to use novel technology to build a TWEANN system, which can be applied to Artificial Life simulation, and Forex trading. Because of Erlang’s architecture, it perfectly matches that of evolutionary and neurocomptational systems. As a programming language, it is a concurrent, message passing paradigm which allows the developers to make full use of the multi-core & multi-cpu systems. Handbook of Neuroevolution Through Erlang explains how to leverage Erlang’s features in the field of machine learning, and the system’s real world applications, ranging from algorithmic financial trading to artificial life and robotics.
Evolutionary Computation for Modeling and Optimization by Daniel Ashlock Pdf
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
Handbook of Natural Computing by Grzegorz Rozenberg,Thomas Bäck,Joost N. Kok Pdf
Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired by nature and also it investigates phenomena taking place in nature in terms of information processing. Examples of the first strand of research covered by the handbook include neural computation inspired by the functioning of the brain; evolutionary computation inspired by Darwinian evolution of species; cellular automata inspired by intercellular communication; swarm intelligence inspired by the behavior of groups of organisms; artificial immune systems inspired by the natural immune system; artificial life systems inspired by the properties of natural life in general; membrane computing inspired by the compartmentalized ways in which cells process information; and amorphous computing inspired by morphogenesis. Other examples of natural-computing paradigms are molecular computing and quantum computing, where the goal is to replace traditional electronic hardware, e.g., by bioware in molecular computing. In molecular computing, data are encoded as biomolecules and then molecular biology tools are used to transform the data, thus performing computations. In quantum computing, one exploits quantum-mechanical phenomena to perform computations and secure communications more efficiently than classical physics and, hence, traditional hardware allows. The second strand of research covered by the handbook, computation taking place in nature, is represented by investigations into, among others, the computational nature of self-assembly, which lies at the core of nanoscience, the computational nature of developmental processes, the computational nature of biochemical reactions, the computational nature of bacterial communication, the computational nature of brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction, and, hence, in terms of computation. We are now witnessing exciting interaction between computer science and the natural sciences. While the natural sciences are rapidly absorbing notions, techniques and methodologies intrinsic to information processing, computer science is adapting and extending its traditional notion of computation, and computational techniques, to account for computation taking place in nature around us. Natural Computing is an important catalyst for this two-way interaction, and this handbook is a major record of this important development.
Handbook of Grammatical Evolution by Conor Ryan,Michael O'Neill,JJ Collins Pdf
This handbook offers a comprehensive treatise on Grammatical Evolution (GE), a grammar-based Evolutionary Algorithm that employs a function to map binary strings into higher-level structures such as programs. GE's simplicity and modular nature make it a very flexible tool. Since its introduction almost twenty years ago, researchers have applied it to a vast range of problem domains, including financial modelling, parallel programming and genetics. Similarly, much work has been conducted to exploit and understand the nature of its mapping scheme, triggering additional research on everything from different grammars to alternative mappers to initialization. The book first introduces GE to the novice, providing a thorough description of GE along with historical key advances. Two sections follow, each composed of chapters from international leading researchers in the field. The first section concentrates on analysis of GE and its operation, giving valuable insight into set up and deployment. The second section consists of seven chapters describing radically different applications of GE. The contributions in this volume are beneficial to both novices and experts alike, as they detail the results and researcher experiences of applying GE to large scale and difficult problems. Topics include: • Grammar design • Bias in GE • Mapping in GE • Theory of disruption in GE · Structured GE · Geometric semantic GE · GE and semantics · Multi- and Many-core heterogeneous parallel GE · Comparing methods to creating constants in GE · Financial modelling with GE · Synthesis of parallel programs on multi-cores · Design, architecture and engineering with GE · Computational creativity and GE · GE in the prediction of glucose for diabetes · GE approaches to bioinformatics and system genomics · GE with coevolutionary algorithms in cybersecurity · Evolving behaviour trees with GE for platform games · Business analytics and GE for the prediction of patient recruitment in multicentre clinical trials
Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation by Samuelson Hong, Wei-Chiang Pdf
Evolutionary computation has emerged as a major topic in the scientific community as many of its techniques have successfully been applied to solve problems in a wide variety of fields. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation provides comprehensive research on emerging theories and its aspects on intelligent computation. Particularly focusing on breaking trends in evolutionary computing, algorithms, and programming, this publication serves to support professionals, government employees, policy and decision makers, as well as students in this scientific field.