Grouping genetic algorithms : advances and applications
This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups...
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| Main Authors | , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Cham, Switzerland :
Springer,
[2016]
|
| Series | Studies in computational intelligence ;
v. 666. |
| Subjects | |
| Online Access | Full text |
| ISBN | 9783319443942 9783319443935 |
| ISSN | 1860-949X ; |
| Physical Description | 1 online resource (xiv, 243 pages) : illustrations |
Cover
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| 100 | 1 | |a Mutingi, Michael, |e author. | |
| 245 | 1 | 0 | |a Grouping genetic algorithms : |b advances and applications / |c Michael Mutingi, Charles Mbohwa. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c [2016] | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource (xiv, 243 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence, |x 1860-949X ; |v volume 666 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Intro; Preface; Contents; Introduction; 1 Exploring Grouping Problems in Industry; 1.1 Introduction; 1.2 Identifying Grouping Problems in Industry; 1.2.1 Cell Formation in Manufacturing Systems; 1.2.2 Assembly Line Balancing; 1.2.3 Job Shop Scheduling; 1.2.4 Vehicle Routing Problem; 1.2.5 Home Healthcare Worker Scheduling; 1.2.6 Bin Packing Problem; 1.2.7 Task Assignment Problem; 1.2.8 Modular Product Design; 1.2.9 Group Maintenance Planning; 1.2.10 Order Batching; 1.2.11 Team Formation; 1.2.12 Earnings Management; 1.2.13 Economies of Scale; 1.2.14 Timetabling | |
| 505 | 8 | |a 1.2.15 Student Grouping for Cooperative Learning1.2.16 Other Problems; 1.3 Extant Modeling Approaches to Grouping Problems; 1.4 Structure of the Book; References; 2 Complicating Features in Industrial Grouping Problems; 2.1 Introduction; 2.2 Research Methodology; 2.3 Research Findings; 2.4 Complicating Features; 2.4.1 Model Conceptualization; 2.4.2 Myriad of Constraints; 2.4.2.1 Intra-Group Relationship; 2.4.2.2 Inter-Group Relationship; 2.4.2.3 Group Size Limits; 2.4.2.4 Grouping Limit; 2.4.3 Fuzzy Management Goals; 2.4.4 Computational Complexity; 2.5 Suggested Solution Approaches | |
| 505 | 8 | |a 2.6 SummaryReferences; Grouping Genetic Algorithms; 3 Grouping Genetic Algorithms: Advances for Real-World Grouping Problems; 3.1 Introduction; 3.2 Grouping Genetic Algorithm: An Overview; 3.2.1 Group Encoding; 3.3 Crossover; 3.3.1 Mutation; 3.3.2 Inversion; 3.4 Grouping Genetic Algorithms: Advances and Innovations; 3.4.1 Group Encoding Strategies; 3.4.1.1 Encoding Strategy 1; 3.4.1.2 Encoding Strategy 2; 3.4.2 Initialization; 3.4.2.1 User-Generated Seeds; 3.4.2.2 Random Generation; 3.4.2.3 Constructive Heuristics; 3.4.3 Selection Strategies; 3.4.3.1 Stochastic Sampling Without Replacement | |
| 505 | 8 | |a 3.4.4 Rank-Based Wheel Selection Strategy3.4.5 Crossover Strategies; 3.4.5.1 Two-Point Group Crossover; 3.4.5.2 Adaptive Crossover; 3.4.6 Mutation Strategies; 3.4.6.1 Swap Mutation; 3.4.6.2 Split Mutation; 3.4.6.3 Merge Mutation; 3.4.6.4 Adaptive Mutation; 3.4.7 Inversion; 3.4.7.1 Two-Point Inversion; 3.4.7.2 Single-Point Inversion; 3.4.7.3 Adaptive Inversion; 3.4.8 Replacement Strategies; 3.4.9 Termination Strategies; 3.4.9.1 Iteration Count (ItCount); 3.4.9.2 Iterations Without Improvement (ItWithoutImp); 3.4.9.3 Hybrid Criteria; 3.5 Application Areas; 3.6 Summary; References | |
| 505 | 8 | |a 4 Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems4.1 Introduction; 4.2 Preliminaries: Fuzzy Logic Control; 4.3 Fuzzy Grouping Genetic Algorithms: Advances and Innovations; 4.3.1 FGGA Coding Scheme; 4.3.2 Initialization; 4.3.3 Fuzzy Fitness Evaluation; 4.3.3.1 Multifactor Evaluation; 4.3.3.2 Fuzzy Goal-Oriented Fitness Evaluation; 4.3.4 Fuzzy Genetic Operators; 4.3.4.1 Fuzzy Controlled Genetic Parameters; Convergence Measure; Diversity Measure; Crossover Probability; Mutation Probability; Inversion Probability; 4.3.4.2 Fuzzy Logic Controlled Crossover | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Genetic algorithms. | |
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| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Mbohwa, Charles, |e author. | |
| 830 | 0 | |a Studies in computational intelligence ; |v v. 666. |x 1860-949X | |
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