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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 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.

THE CROSSOVER MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE CROSSOVER MCQ TO EXPAND YOUR THE CROSSOVER KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY.

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.