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.

Author focuses on the users' needs, providing a coherent view on design elements and working principles Suitable for students and practitioners in computer science, operations research and information systems Book written in an easy-to-read style Includes supplementary material: sn.pub/extras

Autorentext

Prof. Rothlauf completed his PhD thesis in 2001 at the University of Bayreuth, supervised by David E. Goldberg of the Illinois Genetic Algorithms Laboratory (IlliGAL). His particular areas of interest are optimization and heuristics, particulary in the areas of transportation, logistics and business information systems. He chaired the main international conference on evolutionary computing, ACM GECCO, in 2009, and has co-organized many related conferences and workshops; he has also acted as guest editor for related journal special issues; and he published the book "Representations for Genetic and Evolutionary Algorithms" in 2002, this was published in a second edition in 2006.

Inhalt

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