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
∗ Scheduling
∗ 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.

Spis treści:
Foundations of Genetic Algorithms.
Combinatorial Optimization Problems.
Multiobjective Optimization Problems.
Fuzzy Optimization Problems.
Reliability Design Problems.
Scheduling Problems.
Advanced Transportation Problems.
Network Design and Routing.
Manufacturing Cell Design.
References.
Index.

Nota biograficzna:
MITSUO GEN, PhD, is a professor in the Department of Industrial and Information Systems Engineering at the Ashikaga Institute of Technology in Ashikaga, Japan.
RUNWEI CHENG, PhD, is an associate professor in the Institute of Systems Engineering at Northeastern University in Shenyang, China.

Okładka tylna:
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 211;Professors Gen and Cheng provide in–depth coverage of advanced GA techniques for:
∗ Reliability design
∗ Manufacturing cell design
∗ Scheduling
∗ 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.