This book examines one class of combinatorial optimization algorithms -- general iterative non-deterministic algorithms. These algorithms have recently shown significant interest due to their generality, ease of implementation, and the many success stories reporting very positive results. Iterative Computer Algorithms and Their Applications in Engineering uniformly describes five iterative algorithms for solving hard combinatorial optimization problems, namely simulated annealing, genetic algorithms, Tabu search, simulated evolution, and stochastic evolution. It is the only book to describe in a single volume these five main iterative combinatorial algorithms. The introductory chapter motivates the reader to study and use the general iterative approximation algorithms, while introducing the basic terminology. The authors present various iterative techniques and illustrate how they can be applied to solve several NP-hard problems. The book includes case studies of real engineering problems and provides comparative analysis using various techniques that the authors have experimented with and solved. For each algorithm, the authors present the procedures of the algorithm, parameter selection criteria, convergence property analysis, and parallelization. There are also several real-world examples that illustrate various aspects of the algorithms, including real engineering problems. Examples are presented wherever appropriate to illustrate any required mathematical concepts. The book has many unique features -- An integrated and up-to-date description of iterative non-deterministic algorithms -- It is the first book to describe in detail simulated evolution and stochasticevolution -- A brief introduction to fuzzy logic and its application in the formulation of multi-objective optimization problems -- A discussion on hybrid techniques that combine features of heuristics discussed in the book -- A level of treatment suitable for first year graduate stu