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This textbook thoroughly outlines combinatorial algorithms for generation, enumeration, and search. Topics include backtracking and heuristic search methods applied to various combinatorial structures, such as: · Combinations · Permutations · Graphs · Designs · Many classical areas are covered as well as new research topics not included in most existing texts, such as: · Group algorithms · Graph isomorphism · Hill-climbing · Heuristic search algorithms · This work serves as an exceptional textbook for a modern course in combinatorial algorithms, providing a unified and focused collection of recent topics of interest in the area. The authors, synthesizing material that can only be found scattered through many different sources, introduce the most important combinatorial algorithmic techniques - thus creating an accessible, comprehensive text that students of mathematics, electrical engineering, and computer science can understand without needing a prior course on combinatorics

Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas

This book uses the unifying thread of SEARCH as a way of bringing together the major techniques used in symbolic Artificial Intelligence. The authors include program code to illustrate concepts in both POP-11 and Prolog. Each chapter covers a technique and is then divided into three sections: 1) an introduction to the technique; 2) development of a low-level of (POP-11) implementation; and 3) development of a high-level (Prolog) implementation.
This is a practical book which should be valuable to those experienced in artificial intelligence, students with some programming background, and academics and professionals looking for a concise discussion of artificial intelligence through SEARCH