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Applications of multi-objective evolutionary algorithms /
This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hackensack, NJ :
World Scientific,
©2004.
|
| Colección: | Advances in natural computation ;
v. 1. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Applications of multi-objective evolutionary algorithms / |c editors, Carlos A. Coello Coello, Gary B. Lamont. |
| 260 | |a Hackensack, NJ : |b World Scientific, |c ©2004. | ||
| 300 | |a 1 online resource (xxvii, 761 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Advances in natural computation ; |v v. 1 | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a FOREWORD; PREFACE; CONTENTS; CHAPTER 1 AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS; 1.1. Introduction; 1.2. Basic Concepts; 1.3. Basic Operation of a MOEA; 1.4. Classifying MOEAs; 1.4.1. Aggregating Functions; 1.4.2. Population-Based Approaches; 1.4.3. Pareto-Based Approaches; 1.5. MOEA Performance Measures; 1.6. Design of MOEA Experiments; 1.6.1. Reporting MOEA Computational Results; 1.7. Layout of the Book; 1.7.1. Part I: Engineering Applications; 1.7.2. Part II: Scientific Applications; 1.7.3. Part III: Industrial Applications. | |
| 505 | 8 | |a 1.7.4. Part IV: Miscellaneous Applications1.8. General Comments; References; CHAPTER 2 APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN ENGINEERING DESIGN; 2.1. Introduction; 2.2. Multi-Objective Evolutionary Algorithm; 2.2.1. Algorithms; 2.3. Examples; 2.3.1. Design of a Welded Beam; 2.3.2. Preliminary Design of Bulk Carrier; 2.3.3. Design of Robust Airfoil; 2.4. Summary and Conclusions; References; CHAPTER 3 OPTIMAL DESIGN OF INDUSTRIAL ELECTROMAGNETIC DEVICES: A MULTIOBJECTIVE EVOLUTIONARY APPROACH; 3.1. Introduction; 3.2. The Algorithms. | |
| 505 | 8 | |a 3.2.1. Non-Dominated Sorting Evolution Strategy Algorithm (NSESA)3.3. Case Studies; 3.3.1. Shape Design of a Shielded Reactor; 3.3.2. Shape Design of an Inductor for Transverse-Flux-Heating of a Non-Ferromagnetic Strip; 3.4. Conclusions; References; CHAPTER 4 GROUNDWATER MONITORING DESIGN: A CASE STUDY COMBINING EPSILON DOMINANCE ARCHIVING AND AUTOMATIC PARAMETERIZATION ... ; 4.1. Introduction; 4.2. Prior Work; 4.3. Monitoring Test Case Problem; 4.3.1. Test Case Overview; 4.3.2. Problem Formulation; 4.4. Overview of the -NSGA-II Approach; 4.4.1. Searching with the NSGA-II; 4.4.2. Archive Update. | |
| 505 | 8 | |a 4.4.3. Injection and Termination4.5. Results; 4.6. Discussion; 4.7. Conclusions; References; CHAPTER 5 USING A PARTICLE SWARM OPTIMIZER WITH A MULTI-OBJECTIVE SELECTION SCHEME TO DESIGN COMBINATIONAL LOGIC CIRCUITS; 5.1. Introduction; 5.2. Problem Statement; 5.3. Our Proposed Approach; 5.4. Use of a Multi-Objective Approach; 5.5. Comparison of Results; 5.5.1. Example 1; 5.5.2. Example 2; 5.5.3. Example 3; 5.5.4. Example 4; 5.5.5. Example 5; 5.5.6. Example 6; 5.6. Conclusions and Future Work; Acknowledgements; References. | |
| 505 | 8 | |a CHAPTER 6 APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN AUTONOMOUS VEHICLES NAVIGATION6.1. Introduction; 6.2. Autonomous Vehicles; 6.2.1. Experimental Setup; 6.2.2. Vehicle Model; 6.2.3. Relative Sensor Models; 6.2.4. Absolute Sensor Models; 6.2.5. Simulation and Measurement of the Vehicle State; 6.2.6. Prediction of the Vehicle State; 6.3. Parameter Identification of Autonomous Vehicles; 6.3.1. Problem Formulation; 6.3.2. A General Framework for Searching Pareto-Optimal Solutions; 6.3.3. Selection of a Single Solution by CoGM; 6.4. Multi-Objective Optimization. | |
| 520 | |a This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Combinatorial optimization. | |
| 650 | 0 | |a Evolutionary computation. | |
| 650 | 6 | |a Optimisation combinatoire. | |
| 650 | 6 | |a Réseaux neuronaux à structure évolutive. | |
| 650 | 7 | |a Combinatorial optimization |2 fast | |
| 650 | 7 | |a Evolutionary computation |2 fast | |
| 650 | 1 | 7 | |a Genetische algoritmen. |2 gtt |
| 700 | 1 | |a Coello Coello, Carlos A. | |
| 700 | 1 | |a Lamont, Gary B. | |
| 758 | |i has work: |a Applications of multi-objective evolutionary algorithms (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFX8B7mbMfX7crdVV8qDC3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Applications of multi-objective evolutionary algorithms. |d Hackensack, NJ : World Scientific, ©2004 |z 9789812561060 |w (OCoLC)58479902 |
| 830 | 0 | |a Advances in natural computation ; |v v. 1. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=244549 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL244549 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n 188084 | ||
| 994 | |a 92 |b IZTAP | ||