Genetic Algorithms and Engineering Optimization
A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking. Aided by GAs, analysts and designers now routinely evolve solutions to complex combinatorial and multiobjective optimization problems with an ease and rapidity unthinkable withconventional methods. Despite the continued growth and refinement of this powerful analytical tool, there continues to be a lack of up-to-date guides to contemporary GA optimization principles and practices. Written by two of the world's leading experts in the field, this book fills that gap in the literature.
Taking an intuitive approach, Mitsuo Gen and Runwei Cheng employ numerous illustrations and real-world examples to help readers gain a thorough understanding of basic GA concepts-including encoding, adaptation, and genetic optimizations-and to show how GAs can be used to solve an array of constrained, combinatorial, multiobjective, and fuzzy optimization problems. Focusing on problems commonly encountered in industry-especially in manufacturing-Professors Gen and Cheng provide in-depth coverage of advanced GA techniques for:
* Reliability design
* Manufacturing cell design
* Advanced transportation problems
* Network design and routing
Genetic Algorithms and Engineering Optimization is an indispensable working resource for industrial engineers and designers, as well as systems analysts, operations researchers, and management scientists working in manufacturing and related industries. It also makes an excellent primary or supplementary text for advanced courses in industrial engineering, management science, operations research, computer science, and artificial intelligence.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
according activity adaptive alternative applied approach assigned calculated cell chromosome combination combinatorial optimization complete computation connected considered constraints corresponding cost decision maker defined demand denote determine direction edge encoding equal evaluation evolutionary example feasible Figure fitness fitness value fixed follows fuzzy gene genetic algorithms genetic operators given heuristic ideal individuals infeasible initial integer linear machine matrix maximum means membership method minimize mutation node objective function obtained offspring operator optimal solution optimization problems otherwise parameters parent Pareto solutions path performance permutation population positive possible preference probability problem procedure production programming proposed random randomly reliability representation represented resource roulette wheel selection route runs schedule selection sequence shown in Figure solving space spanning tree Step strategy string Table techniques transportation usually variables weight
Page 460 - A new representation and operators for genetic algorithms applied to grouping problems.