Most textbooks on modern heuristics provide the reader with detailed descriptions of the functionality of single examples like genetic algorithms, genetic programming, tabu search, simulated annealing, and others, but fail to teach the underlying concepts behind these different approaches.The author takes a different approach in this textbook by focusing on the users' needs and answering three fundamental questions: First, he tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. Second, he teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles. Third, he shows how we can make use of problem-specific knowledge for the design of efficient and effective modern heuristics that solve not only small toy problems but also perform well on large real-world problems.This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.

Many books describe the application of heuristic optimization methods to a wide variety of optimization problems. However, the process of developing an efficient optimization method for the specific problem at hand seems to be considered a "black art", the domain of optimization specialists. This book focuses on the practitioner and demonstrates how to apply heuristic optimization methods. It is the first book to offer a systematic theory of how to apply and adapt heuristic optimization methods. It breaks down the art of designing efficient optimization methods into engineering tasks, and examines how to build successful applications of heuristic optimization methods. In so doing the reader learns how to properly model the problem, choose the optimization method, adapt that method to the properties of the specific problem, and, finally, implement the method.

Chap. 1.- Introduction.- Part I.- Fundamentals.- Chap. 2.- Optimization Problems.- Chap. 3.- Optimization Methods.- Part II.- Modern Heuristics.- Chap. 4.- Design Elements.- Chap. 5.- Search Strategies.- Chap. 6.- Design Principles.- Part III Case Studies.- Chap. 7.- High Locality Representations for Automated Programming.- Chap. 8.-Biased Modern Heuristics for the OCST Problem.- Chap. 9.-Summary.- References.- Nomenclature.- Glossary.- Index