Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It is a carefully structured and integrated treatment of the major technologies in optimization and search methodology. The book is made up of 19 chapters. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world’s leading authorities in their field.
The result is a major state-of-the-art tutorial text of the main optimization and search methodologies available to researchers, students and practitioners across discipline domains in applied science. It can be used as a textbook or a reference book to learn and apply these methodologies to a wide range of today’s problems. It has been written by some of the world’s most well known authors in the field.
Table of Content
Classical Techniques.- Integer Programming.- Genetic Algorithms.- Genetic Programming.- Tabu Search.- Simulated Annealing.- Variable Neighborhood Search.- Constraint Programming.- Multi-Objective Optimization.- Complexity Theory and the No Free Lunch Theorem.- Machine Learning.- Artificial Immune Systems.- Swarm Intelligence.- Fuzzy Reasoning.- Rough Set Based Decision Support.- Hyper-Heuristics.- Approximation Algorithms.- Fitness Landscapes.
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