EC&AI Books

"Evolutionary Computation for Modeling and Optimization"Daniel AshlockSpringer ISBN 0387221964 2005 Year PDF 3,04 Mb 572 Pages

“ Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered.


"Practical Genetic Algorithms" (2nd edition)Randy L. Haupt, Sue Ellen HauptWiley-Interscience ISBN 0471455652 2004 Year PDF 2,4 Mb 272 Pages

“the first introductory-level book to emphasize practical applications through the use of example problems..."(International Journal of General Systems, Vol. 31, No. 1, 2002)— This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science— Most significant update to the second edition is the MATLAB codes that accompany the text— Provides a thorough discussion of hybrid genetic algorithms— Features more examples than first edition ”


"Foundations of Soft Case-Based Reasoning"Simon Shiu, Sankar K. PalWiley-Interscience ISBN 0471086355 2004 Year PDF 1,6 Mb 274 Pages

“ A breakthrough on today s fastest growing artificial intelligence technique Many of today s engineering and business computer applications require decisions to be made on the basis of uncertain or incomplete information, a phenomenon that has resulted in the development of case-based reasoning, a powerful computing technique by which a system s problem-solving ability is enhanced by reference to its previously stored experiences. Foundations of Soft Case-Based Reasoning is the first book of its kind to provide a unified framework for understanding how soft computing techniques can be used to build and maintain case-based reasoning (CBR) systems. Written by two internationally renowned experts, the book demonstrates the latest advances of machine learning and intelligence and presents CBR methodologies and algorithms designed to be useful for both students of artificial intelligence and practitioners in the field. Structured according to the four major phases of the problem-solving process of a CBR system: representation and indexing of cases, case selection and retrieval, case adaptation, and case-base maintenance; the authors provide a solid foundation of the subject with a balanced mix of theory, algorithms, and application.

«Frontiers of Evolutionary Computation»Anil Menon (Editor)Springer ISBN 1402075243 2004 Year PDF 2,98 Mb 272 Pages

“ Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (EC). They explore such topics as the role of building blocks, the balancing of exploration with exploitation, the modeling of EC algorithms, the connection with optimization theory and the role of EC as a meta-heuristic method, to name a few. The articles feature a mixture of informal discussion interspersed with formal statements, thus providing the reader an opportunity to observe a wide range of EC problems from the investigative perspective of world-renowned researchers. Frontiers of Evolutionary Computation is ideal for researchers and students who want to follow the process of EC problem-solving and for those who want to consider what frontiers still await their exploration. ”


Evolutionary Dynamics and Extensive Form Games (Economic Learning and Social Evolution) - Ross CressmanMIT Press PDF 327 Pages

Evolutionary game theory attempts to predict individual behavior (whether of humans or other species) when interactions between individuals are modeled as a noncooperative game. Most dynamic analyses of evolutionary games are based on their normal forms, despite the fact that many interesting games are specified more naturally through their extensive forms. Because every extensive form game has a normal form representation, some theorists hold that the best way to analyze an extensive form game is simply to ignore the extensive form structure and study the game in its normal form representation. This book rejects that suggestion, arguing that a game's normal form representation often omits essential information from the perspective of dynamic evolutionary game theory. The book offers a synthesis of current knowledge about extensive form games from an evolutionary perspective, emphasizing connections between the extensive form representation and dynamic models that traditionally have been applied to biological and economic phenomena. It develops a general theory to analyze dynamically arbitrary extensive form games and applies this theory to a range of examples. It lays the foundation for the analysis of specific extensive form models of behavior and for the further theoretical study of extensive form evolutionary games.


Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Zbigniew ISBN: 3-540-60676-9

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.


Evolutionary Algorithms in Theory and Practice : Evolution Strategies, Evolutionary Programming, Genetic Algorithms
328 pages Oxford University Press USA ISBN: 0195099710

This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed.


Evolutionary Computation 1: Basic Algorithms and Operators (Evolutionary Computation)
339 pages Taylor & Francis ISBN: 0750306645

The field of evolutionary computation is expanding dramatically, fueled by vast investment reflecting the value of applications of its techniques.The number of courses offered in evolutionary computing is growing rapidly: reflecting demand for material published in Handbook of Evolutionary Computation to be published in units suitable for use by students and teachers as well as individual researchers, we are publishing two volumes on algorithms and operators. The books contain material which has been brought right up to date, as well as some new contributions.This volume discusses the basic ideas which underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, genetic programming. It is intended to be used by individual researchers and by teachers and students in the expanding field of evolutionary computation.


"Evolutionary Computation 2 (Advanced Algorithms and Operators)
270 pages Taylor & Francis ISBN: 0750306653

This volume expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation and implementation of evolutionary algorithms. It is intended to be used by individual researchers and by teachers and students in the expanding field of evolutionary computation.


"Swarm Intelligence"James Kennedy, Russell C. Eberhart, with Yuhui ShiThe Morgan Kaufmann Series in Evolutionary Computation ISBN 1-55860-595-9 © 2001 by Academic Press PDF 541 PagesSeries Editor: David B. Fogel

Traditional methods for creating intelligent computational systems haveprivileged private "internal" cognitive and computational processes. Incontrast, Swarm Intelligence argues that humanintelligence derives from the interactions of individuals in a social worldand further, that this model of intelligence can be effectively applied toartificially intelligent systems. The authors first present the foundations ofthis new approach through an extensive review of the critical literature insocial psychology, cognitive science, and evolutionary computation. Theythen show in detail how these theories and models apply to a newcomputational intelligence methodologyparticle swarmswhich focuseson adaptation as the key behavior of intelligent systems. Drilling downstill further, the authors describe the practical benefits of applying particleswarm optimization to a range of engineering problems. Developed bythe authors, this algorithm is an extension of cellular automata andprovides a powerful optimization, learning, and problem solving method.


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