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E-raamat: Genetic Algorithms & Engineering Optimization [Wiley Online]

(Ashikaga Institute of Technology, Japan), (Ashikaga Institute of Technology, Japan)
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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.
Preface xiii
Foundations of Genetic Algorithms
1(52)
Introduction
1(13)
Encoding Issue
2(5)
Genetic Operators
7(2)
Selection
9(2)
Genetic Local Search
11(3)
Adaptation of Genetic Algorithms
14(13)
Structure Adaptation
15(1)
Parameter Adaptation
16(2)
Fuzzy Logic Controller
18(9)
Genetic Optimizations
27(13)
Global Optimizations
27(7)
Constrained Optimizations
34(4)
Combinatorial Optimizations
38(1)
Multiobjective Optimizations
39(1)
Recent Genetic Algorithm Dissertations
40(13)
Combinatorial Optimization Problems
53(44)
Introduction
53(1)
Set-Covering Problem
53(8)
Airline Crew Scheduling Problems
56(1)
Genetic Representation
56(2)
Genetic Operators
58(2)
Genetic Algorithm
60(1)
Computational Experience
61(1)
Bin-Packing Problem
61(10)
Heuristic Algorithms
63(2)
Genetic Representation
65(3)
Genetic Operators
68(1)
Fitness Function
69(1)
Initial Population
69(1)
Computational Experience
70(1)
Knapsack Problem
71(10)
Multiple-Choice Knapsack Problem
72(5)
Multiconstraint Knapsack Problem
77(4)
Minimum Spanning Tree Problem
81(16)
Quadratic Minimum Spanning Tree Problem
82(3)
Degree-Constrained Minimum Spanning Tree Problem
85(5)
Bicriteria Minimum Spanning Tree Problem
90(7)
Multiobjective Optimization Problems
97(45)
Introduction
97(1)
Basic Concepts of Multiobjective Optimizations
97(9)
Nondominated Solutions
98(3)
Preference Structures
101(1)
Basic Solution Approaches
102(4)
Structures and Properties of Problems
106(1)
Genetic Multiobjective Optimization
106(9)
Features of Genetic Search
106(1)
Fitness Assignment Mechanism
107(4)
Fitness Sharing and Population Diversity
111(2)
The Concept of Pareto Solution
113(2)
Vector-Evaluated Genetic Algorithms
115(3)
Pareto Ranking and Tournament Methods
118(6)
Pareto Ranking Method
118(4)
Pareto Tournament Method
122(2)
Weighted-Sum Approach
124(7)
Random-Weight Approach
125(2)
Adaptive Weight Approach
127(4)
Distance Method
131(5)
General Idea of the Distance Method
131(2)
Calculation of Distance Measure
133(3)
Application of the Distance Method
136(1)
Compromise Approach
136(2)
Goal Programming Approach
138(4)
Fuzzy Optimization Problems
142(52)
Introduction
142(1)
Fuzzy Linear Programming
143(13)
Fuzzy Linear Programming Model
143(6)
Genetic Algorithm Approach
149(3)
Interactive Approach
152(2)
Numerical Example
154(2)
Fuzzy Nonlinear Programming
156(9)
Nonlinear Programming Model
157(4)
Inexact Approach to FO/RNP-1
161(2)
Interactive Approach
163(1)
Numerical Example
164(1)
Fuzzy Nonlinear Mixed-Integer Goal Programming
165(13)
Fuzzy Nonlinear Mixed-Integer Goal Programming Model
168(2)
Genetic Algorithm Approach
170(3)
Numerical Examples
173(5)
Fuzzy Multiobjective Integer Programming
178(16)
Problem Formulation
181(3)
Augmented Minimax Problems
184(1)
Genetic Algorithm Approach
185(4)
Interactive Fuzzy Satisfaction Method
189(1)
Numerical Example
190(4)
Reliability Design Problems
194(41)
Introduction
194(1)
Network Reliability Design
195(16)
Problem Formulation
196(2)
Dengiz, Altiparmak, and Smith's Approach
198(6)
Deeter and Smith's Approach
204(7)
Tree-Based Network Reliability and LAN Design
211(10)
Bicriteria Network Topology Design
212(7)
Numerical Examples
219(2)
Multiobjective Reliability Design
221(14)
Bicriteria Reliability Design
221(3)
Genetic Algorithm Approach
224(2)
Hybrid Genetic Algorithm Approach
226(4)
Reliability Design with Fuzzy Goals
230(5)
Scheduling Problems
235(62)
Introduction
235(1)
Job-Shop Scheduling
235(18)
Basic Approaches
236(1)
Encodings
236(2)
Adapted Genetic Operators
238(4)
Heuristic-Featured Genetic Operators
242(2)
Hybrid Genetic Algorithms
244(7)
Discussion
251(2)
Grouped Job Scheduling Problem
253(10)
Problem Description and Necessary Condition
253(2)
Fundamental Runs
255(2)
Representation
257(2)
Evaluation
259(1)
Genetic Operators
260(1)
Overall Procedure
260(1)
Numerical Example
261(2)
Resource-Constrained Project Scheduling
263(15)
Priority-Based Encoding
265(5)
Genetic Operators
270(2)
Evaluation and Selection
272(2)
Experimental Results
274(4)
Parallel Machine Scheduling
278(10)
Dominance Condition
280(4)
Memetic Algorithms
284(3)
Experimental Results
287(1)
Multiprocessor Scheduling
288(9)
Problem Description and Assumptions
289(1)
Genetic Algorithm for MSP
290(5)
Numerical Example
295(2)
Advanced Transportation Problems
297(44)
Introduction
297(7)
Transportation Model
297(2)
Formulation of Transportation Problems
299(5)
Spanning Tree-Based Approach
304(7)
Tree Representation
304(2)
Initialization
306(2)
Genetic Operators
308(1)
Evaluation and Selection
308(3)
Overall Procedure
311(1)
Multiobjective Transportation Problem
311(9)
Problem Description
312(1)
Spanning Tree--Based Genetic Algorithm for Multiobjective Transportation Problem
313(3)
Numerical Examples
316(4)
Fixed-Charge Transportation Problem
320(5)
Mathematical Model
321(1)
Difficulty of fcTP Instances
321(1)
Solution Method for the fcTP
322(1)
Implementation of the Genetic Algorithm
322(1)
Numerical Examples
323(2)
Capacitated Plant Location Problem
325(5)
Mathematical Model
326(2)
Spanning Tree--Based Genetic Algorithm for Plant Location Problems
328(1)
Numerical Examples
329(1)
Bicriteria Transportation Problem with Fuzzy Coefficients
330(11)
Problem Description
331(1)
Ranking Fuzzy Numbers
332(1)
Implementation of the Genetic Algorithm
333(3)
Numerical Examples
336(5)
Network Design and Routing
341(49)
Introduction
341(1)
Shortest Path Problem
341(11)
Problem Formulation
342(1)
Genetic Algorithm Approach
343(8)
Numerical Examples
351(1)
Adaptive Network Routing
352(11)
Genetic-Based Adaptive Routing
353(1)
Representation of Chromosomes
354(1)
Evaluation of Chromosomes
355(1)
Genetic Operators
355(5)
Numerical Examples
360(3)
Centralized Network Design
363(3)
Problem Formulation
364(1)
Genetic Algorithm Approach
365(1)
Numerical Example
366(1)
Computer Network Expansion
366(7)
Problem Description
368(1)
Kummar, Pathak, and Gupta's Approach
369(4)
Numerical Example
373(1)
Multistage Process Planning
373(5)
Problem Description
374(2)
Genetic Algorithm Approach
376(1)
Numerical Examples
377(1)
M/G/s Queuing Facility Location on a Network
378(12)
Problem Description
379(4)
Evolutionary Computation Approach
383(4)
Numerical Examples
387(3)
Manufacturing Cell Design
390(61)
Introduction
390(1)
Manufacturing Cell Design
391(2)
Traditional MCD Approaches
393(3)
Similarity Coefficient Methods
394(1)
Array-Based Methods
395(1)
Mathematical Programming Methods
395(1)
Graph and Network Methods
396(1)
Genetic Algorithm Approaches
396(23)
Representative and Genetic Operators
396(3)
Joines' Order-Based Approach
399(6)
Moon and Kim's Approach
405(5)
Joines' Integer Programming Formulation Approach
410(9)
Other Approaches
419(1)
Cell Design with Alternative Process Plans
419(19)
Incorporation of Alternative Operations and Machine Redundancy
421(3)
Incorporation of Alternative Routings
424(9)
Moon, Gen, and Kim's Approach to Independent Cells
433(5)
Designing Independent Cells
438(13)
Family Formation for Minimizing Number of Machine Types
439(4)
Determining Number of Families
443(5)
Minimizing Number of Machines
448(1)
Other Considerations
449(2)
References 451(40)
Index 491


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.