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Iba, H. et al. CRC Press 2009.8 * Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. * Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website. * Serra, R. et al. ed. * The Italian community in Artificial Life and Evolutionary computation has grown remarkably in recent years, and this book is the first broad collection of its major interests and achievements (including contributions from foreign countries). The contributions in Artificial Life as well as in Evolutionary Computation allow one to see the deep connections between the two fields. The topics addressed are extremely relevant for present day research in Artificial Life and in Evolutionary Computation, which include important contributions from very well-known researchers. The volume provides a very broad picture of the Italian activities in this field. * Kulkarni, V. G. * Based on the author's more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. Along with reorganizing the material, this edition revises and adds new exercises and examples. * Solnon, C. Wiley-ISTE 2010.5 * The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. * Shen, A. Springer-Verlag 2009.12 * "Algorithms and Programming" is primarily intended for a first year undergraduate course in programming. Structured in a problem-solution format, the text motivates the student to think through the programming process, thus developing a firm understanding of the underlying theory. Although a moderate familiarity with programming is assumed, the book is easily utilized by students new to computer science. The more advanced chapters make the book useful for a graduate course in the analysis of algorithms and/or compiler construction. * Leung, C.- S. et al. ed. * The 145 regular session papers and 53 special session papers presented were carefully reviewed and selected from 466 submissions. The papers are structured in topical sections on cognitive science and computational neuroscience, neurodynamics, mathematical modeling and analysis, kernel and related methods, learning algorithms, pattern analysis, face analysis and processing, image processing, financial applications, computer vision, control and robotics, evolutionary computation, other emerging computational methods, signal, data and text processing, artificial spiking neural systems: nonlinear dynamics and engineering applications, towards brain-inspired systems, computational advances in bioinformatics, data mining for cybersecurity, evolutionary neural networks: theory and practice, hybrid and adaptive systems for computer vision and robot control, intelligent data mining, neural networks for data mining, and SOM and related subjects and its applications. * Zelinka, I. et al. ed. * This book discusses the mutual intersection of two interesting fields of research, i.e. deterministic chaos and evolutionary computation. Evolutionary computation which are able to handle tasks such as control of various chaotic systems and synthesis of their structure are explored, while deterministic chaos is investigated as a behavioral part of evolutionary algorithms. * |
