## Genetic Algorithms and Engineering OptimizationA 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. |

### From inside the book

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Page 4

A general discussion of multidimensional encoding and crossover was

A general discussion of multidimensional encoding and crossover was

**given**by Bui and Moon [ 79 ] , who argued that to fit solutions of multidimensional problems into onedimensional encoding entails losing a considerable amount of the ...Page 346

For a

For a

**given**partial path Pk , the set of eligible edges Eq is**given**as follows : Eq = T , n C ( Vp , V ) ( 8.9 ) Now we consider how to find the set of Eq . For a connected and undirected graph G = ( V , E ) , the adjacency matrix A is ...Page 387

The global optimal solutions ( scopes ) are

The global optimal solutions ( scopes ) are

**given**in Table 8.9 , where ( 0 ) ° indicates the global optimal solution ( scope ) , ( ) indicates the best solution among 30 runs of ECA , and the time is the actual computing time in seconds ...### What people are saying - Write a review

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### Contents

Combinatorial Optimization Problems | 53 |

Multiobjective Optimization Problems | 97 |

Fuzzy Optimization Problems | 142 |

Copyright | |

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### Common terms and phrases

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 solve space Step strategy string Table techniques transportation usually variables weight