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The aim of the book is to lay out the foundations and provide a detailed treatment of the subject. It will focus on two main elements in dual phase evolution: the relationship between dual phase evolution and other phase transition phenomena and the advantages of dual phase evolution in evolutionary computation and complex adaptive systems. The book will provide a coherent picture of dual phase evolution that encompasses these two elements and frameworks, methods and techniques to use this concept for problem solving.
This volume constitutes the proceedings of the 7th International Conference on Simulated Evolution and Learning, SEAL 2008, held in Melbourne, Australia, during December 7-10, 2008. The 65 papers presented were carefully reviewed and selected from 140 submissions. The topics covered are evolutionary learning; evolutionary optimisation; hybrid learning; adaptive systems; theoretical issues in evolutionary computation; and real-world applications of evolutionary computation techniques.
This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
This 2006 work began with the author's exploration of the applicability of the finite deformation theory of elasticity when various standard assumptions such as convexity of various energies or ellipticity of the field equations of equilibrium are relinquished. The finite deformation theory of elasticity turns out to be a natural vehicle for the study of phase transitions in solids where thermal effects can be neglected. This text will be of interest to those interested in the development and application of continuum-mechanical models that describe the macroscopic response of materials capable of undergoing stress- or temperature-induced transitions between two solid phases. The focus is on the evolution of phase transitions which may be either dynamic or quasi-static, controlled by a kinetic relation which in the framework of classical thermomechanics represents information that is supplementary to the usual balance principles and constitutive laws of conventional theory.
The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.
What Is Evolutionary Computation The term "evolutionary computation" refers to both a subfield of artificial intelligence and soft computing in the field of computer science that studies various optimization algorithms and a family of algorithms for global optimization that are inspired by biological evolution. They belong to a family of problem solvers known as population-based trial and error problem solvers, and they either have a metaheuristic or stochastic optimization flavor. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Evolutionary computation Chapter 2: Differential evolution Chapter 3: Dual-phase evolution Chapter 4: Evolutionary algorithm Chapter 5: Evolutionary programming Chapter 6: Neuroevolution Chapter 7: Evolutionary robotics Chapter 8: Grammatical evolution Chapter 9: Human-based evolutionary computation Chapter 10: Interactive evolutionary computation (II) Answering the public top questions about evolutionary computation. (III) Real world examples for the usage of evolutionary computation in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of evolutionary computation' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of evolutionary computation.
Of what use is evolutionary science to society? Can evolutionary thinking provide us with the tools to better understand and even make positive changes to the world? Addressing key questions about the development of evolutionary thinking, this book explores the interaction between evolutionary theory and its practical applications. Featuring contributions from leading specialists, Pragmatic Evolution highlights the diverse and interdisciplinary applications of evolutionary thinking: their potential and limitations. The fields covered range from palaeontology, genetics, ecology, agriculture, fisheries, medicine, neurobiology, psychology and animal behaviour; to information technology, education, anthropology and philosophy. Detailed examples of useful and current evolutionary applications are provided throughout. An ideal source of information to promote a better understanding of contemporary evolutionary science and its applications, this book also encourages the continued development of new opportunities for constructive evolutionary applications across a range of fields.
The present book includes a set of selected extended papers from the first International Joint Conference on Computational Intelligence (IJCCI 2009), held in Madeira, Portugal, from 5 to 7 October 2009. The conference was composed by three co-located conferences: The International Conference on Fuzzy Computation (ICFC), the International Conference on Evolutionary Computation (ICEC), and the International Conference on Neural Computation (ICNC). Recent progresses in scientific developments and applications in these three areas are reported in this book. IJCCI received 231 submissions, from 35 countries, in all continents. After a double blind paper review performed by the Program Committee, only 21 submissions were accepted as full papers and thus selected for oral presentation, leading to a full paper acceptance ratio of 9%. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of IJCCI 2009. Commitment to high quality standards is a major concern of IJCCI that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.
What Is Simulated Annealing The method of simulated annealing, often known as SA, is a probabilistic approach that can approximate the value of a function's global optimal value. To be more specific, it is a metaheuristic that allows for an approximation of global optimization in a vast search space when dealing with an optimization problem. The global optimal solution can be found using SA for large numbers of local optimal solutions. It is utilized quite frequently in situations in which the search space is discrete. Simulated annealing may be superior to exact algorithms like gradient descent and branch and bound for solving problems where obtaining an approximate global optimum is more important than finding a precise local optimum in a set amount of time. This is the case when finding an approximate global optimum is more important. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Simulated annealing Chapter 2: Adaptive simulated annealing Chapter 3: Automatic label placement Chapter 4: Combinatorial optimization Chapter 5: Dual-phase evolution Chapter 6: Graph cuts in computer vision Chapter 7: Molecular dynamics Chapter 8: Multidisciplinary design optimization Chapter 9: Particle swarm optimization Chapter 10: Quantum annealing (II) Answering the public top questions about simulated annealing. (III) Real world examples for the usage of simulated annealing in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of simulated annealing' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of simulated annealing.