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Adaptive and Natural Computing Algorithms
The papers in this volume present theoretical insights and report practical applications both for neural networks, genetic algorithms and evolutionary computation. In the field of natural computing, swarm optimization, bioinformatics and computational biology contributions are no less compelling. A wide selection of contributions report applications of neural networks to process engineering, robotics and control. Contributions also abound in the field of evolutionary computation particularly in combinatorial and optimization problems. Many papers are dedicated to machine learning and heuristics, hybrid intelligent systems and soft computing applications. Some papers are devoted to quantum computation. In addition, kernel based algorithms, able to solve tasks other than classification, represent a revolution in pattern recognition bridging existing gaps. Further topics are intelligent signal processing and computer vision.
Applications of Learning Classifier Systems
The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw a...
Circuit Analysis
This text presents the fundamentals of circuit analysis in a way suitable for first and second year undergraduate courses in electronic or electrical engineering. It is very much a 'theme text' and not a work book. The author is at pains to follow the logical thread of the subject, showing that the development of topics, one from the other, is not ad hoc as it can sometimes appear. A case in point is the application of graph theory to justify the derivation of the Node- and Mesh-equations from the more extensive set of Kirchhoff current and voltage equations. The topology of networks is stressed, again with the aid of graph theory.The Fourier series is discussed at an early stage in regard t...
Learning Classifier Systems
The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er sy...
Learning Classifier Systems
Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
Artificial Intelligence, Automated Reasoning, and Symbolic Computation
AISC 2002, the 6th international conference on Arti?cial Intelligence and S- bolic Computation, and Calculemus 2002, the 10th symposium on the Integ- tion of Symbolic Computation and Mechanized Reasoning, were held jointly in Marseille, France on July 1-5, 2002. This event was organized by the three universities in Marseille together with the LSIS (Laboratoire des Sciences de l'Information et des Syst` emes). AISC 2002 was the latest in a series of specialized conferences founded by John Campbell and Jacques Calmet with the initial title "Arti?cial Intelligence and Symbolic Mathematical Computation" (AISMC) and later denoted "Art- cial Intelligence and Symbolic Computation" (AISC). The scope...
Genetic Programming
This volume contains the proceedings of EuroGP 2000, the European Conf- ence on Genetic Programming, held in Edinburgh on the 15th and 16th April 2000. This event was the third in a series which started with the two European workshops: EuroGP’98, held in Paris in April 1998, and EuroGP’99, held in Gothenburg in May 1999. EuroGP 2000 was held in conjunction with EvoWo- shops 2000 (17th April) and ICES 2000 (17th-19th April). Genetic Programming (GP) is a growing branch of Evolutionary Compu- tion in which the structures in the population being evolved are computer p- grams. GP has been applied successfully to a large number of di?cult problems like automatic design, pattern recognition, r...
Computer Animation and Simulation ’99
The 20 research papers in this volume demonstrate novel models and concepts in animation and graphics simulation. Special emphasis is given on innovative approaches to Modelling Human Motion, Models of Collision Detection and Perception, Facial Animation and Communication, Specific Animation Models, Realistic Rendering for Animation, and Behavioral Animation.
Inverse Problems in Medical Imaging and Nondestructive Testing
14 contributions present mathematical models for different imaging techniques in medicine and nondestructive testing. The underlying mathematical models are presented in a way that also newcomers in the field have a chance to understand the relation between the special applications and the mathematics needed for successfully treating these problems. The reader gets an insight into a modern field of scientific computing with applications formerly not presented in such form, leading from the basics to actual research activities.
Surface Topology
This updated and revised edition of a widely acclaimed and successful text for undergraduates examines topology of recent compact surfaces through the development of simple ideas in plane geometry. Containing over 171 diagrams, the approach allows for a straightforward treatment of its subject area. It is particularly attractive for its wealth of applications and variety of interactions with branches of mathematics, linked with surface topology, graph theory, group theory, vector field theory, and plane Euclidean and non-Euclidean geometry. - Examines topology of recent compact surfaces through the development of simple ideas in plane geometry - Contains a wealth of applications and a variety of interactions with branches of mathematics, linked with surface topology, graph theory, group theory, vector field theory, and plane Euclidean and non-Euclidean geometry
