The field of optimization is interdisciplinary in nature, and has made a significant impact on many areas of technology. As a result, optimization is an indispensable tool for many practitioners in various fields. Conventional optimization techniques are well established and widely published in many excellent textbooks. However, there are new techniques, such as simulated annealing, mean field theory, and genetic algorithms, which have proven to be effective in solving global optimization problems. Computational Intelligence for Optimization is intended as a technical description of the state-of-the-art developments in advanced optimization techniques, specifically simulated annealing, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementation, and practical applications. Most of the theories covered in this work and their applications are widely scattered in journals, technical reports, and conference proceedings of various fields, making it difficult for people new in the field to find easily. The authors have brought together a comprehensive and organized treatment of these techniques, thus filling a gap in the scientific literature. Computational Intelligence for Optimization is suitable for first-year graduate courses in electrical and computer engineering, computer science, and operational research programs. It may also be used as a reference work for practising engineers, scientists, operational researchers and other specialists.