Crossover for single-objective numerical optimization problems


Book Description

This book is the first of the series of reference books I am working on, with the aim to provide a possibly most comprehensive review of methods developed in the field of Genetic Algorithms. The necessity to concentrate on certain thematic areas is the result of the character of these books. The choice of those areas, even though performed arbitrarily will hopefully reflect their degree of importance and popularity. Hence, in this book which begins the whole series, an operator of the greatest importance for Genetic Algorithms will be presented i.e. crossover operator and its area of application will be single objective numerical optimization problems. This edition contains descriptions of 11 standard, 66 binary coded, and 89 real coded crossover operators; 182 algorithms in a form of pseudo code; and 453 active URLs pointing to sites with referenced papers. My Internet page (www.tomaszgwiazda.pl) offers the first 40 pages of this book. You can also find a review written for Polish edition of my work.




Crossover


Book Description

Crossover is a laboratory manual and computer program that work together to teach the principles of genetics. Designed to complement regular textbooks and classroom instruction, Crossover consists of thirty-five modules that can be tailored to fit genetics courses at several levels. Examples, interactive computer models, problems, and self-tests all help students understand difficult concepts and learn the basic mathematical skills needed to study contemporary theories of genetics, evolution, and breeding. The easy-to-use tutorial system lets students work at their own pace. Features include: - In-depth investigations of meiosis, genetic ratios, linkage mutation, natural selection, Hardy-Weinberg equilibrium, artificial selection, quantitative genetics, breeding methods, mating designs, plant patent law, and the use of molecular markers - A computer model that allows students to manipulate genetic parameters and compare outcomes. Students can observe evolution and artificial selection in action - A "Major Concepts" section at the beginning of each chapter to help students focus on the important material to be learned - A visual, easy-to-understand presentation of material - Exercises based on genetic data and analyses from actual research projects - Several stages of complexity within each area of instruction. - Instant grading of exercises - "Suggested Readings" at the end of each chapter to direct the student to related books, articles, and computer programs.







Automatic Generation Of Neural Network Architecture Using Evolutionary Computation


Book Description

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.




Botanical Gazette


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Botanical Abstracts


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VLSI Design and Test


Book Description

This book constitutes the refereed proceedings of the 17th International Symposium on VLSI Design and Test, VDAT 2013, held in Jaipur, India, in July 2013. The 44 papers presented were carefully reviewed and selected from 162 submissions. The papers discuss the frontiers of design and test of VLSI components, circuits and systems. They are organized in topical sections on VLSI design, testing and verification, embedded systems, emerging technology.







Evolutionary Computation


Book Description

This Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.