Applications of multi-objective evolutionary algorithms [[electronic resource] /] / editors, Carlos A. Coello Coello, Gary B. Lamont

• Record Nr.: UNINA9910450440603321
• Pubbl/distr/stampa: Singapore ; ; Hackensack, NJ, : World Scientific, c2004
• ISBN: 1-281-88084-1; 9786611880842; 981-256-779-8
• Descrizione fisica: 1 online resource (XXVII, 761 p.)
• Collana: Advances in natural computation ; ; v. 1
• Altri autori (Persone): Coello CoelloCarlos A; LamontGary B
• Disciplina: 519.3
• Soggetti: Combinatorial optimization; Evolutionary computation; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: FOREWORD; PREFACE; CONTENTS; CHAPTER 1 AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS; CHAPTER 2 APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN ENGINEERING DESIGN; CHAPTER 3 OPTIMAL DESIGN OF INDUSTRIAL ELECTROMAGNETIC DEVICES: A MULTIOBJECTIVE EVOLUTIONARY APPROACH; CHAPTER 4 GROUNDWATER MONITORING DESIGN: A CASE STUDY COMBINING EPSILON DOMINANCE ARCHIVING AND AUTOMATIC PARAMETERIZATION...; CHAPTER 5 USING A PARTICLE SWARM OPTIMIZER WITH A MULTI-OBJECTIVE SELECTION SCHEME TO DESIGN COMBINATIONAL LOGIC CIRCUITS; CHAPTER 6 APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN AUTONOMOUS VEHICLES NAVIGATIONCHAPTER 7 AUTOMATING CONTROL SYSTEM DESIGN VIA A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM; CHAPTER 8 THE USE OF EVOLUTIONARY ALGORITHMS TO SOLVE PRACTICAL PROBLEMS IN POLYMER EXTRUSION; CHAPTER 9 EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION OF TRUSSES; CHAPTER 10 CITY AND REGIONAL PLANNING VIA A MOEA: ; LESSONS LEARNED; CHAPTER 11 A MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR THE COVERING TOUR PROBLEM; CHAPTER 12 A COMPUTER ENGINEERING BENCHMARK APPLICATION FOR MULTIOBJECTIVE OPTIMIZERS; CHAPTER 13 MULTIOBJECTIVE AERODYNAMIC DESIGN AND VISUALIZATION OF SUPERSONIC WINGS BY USING ADAPTIVE RANGE MULTIOBJECTIVE...CHAPTER 14 APPLICATIONS OF A MULTI-OBJECTIVE GENETIC ALGORITHM IN CHEMICAL AND ENVIRONMENTAL ENGINEERING; CHAPTER 15 MULTI-OBJECTIVE SPECTROSCOPIC DATA ANALYSIS OF INERTIAL CONFINEMENT FUSION IMPLOSION CORES: PLASMA GRADIENT...; CHAPTER 16 APPLICATION OF MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION ALGORITHMS IN MEDICINE; CHAPTER 17 ON MACHINE LEARNING WITH MULTIOBJECTIVE GENETIC OPTIMIZATION; CHAPTER 18 GENERALIZED ANALYSIS OF PROMOTERS: A METHOD FOR DNA SEQUENCE DESCRIPTION; CHAPTER 19 MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR COMPUTER SCIENCE APPLICATIONSCHAPTER 20 DESIGN OF FLUID POWER SYSTEMS USING A MULTI OBJECTIVE GENETIC ALGORITHM; CHAPTER 21 ELIMINATION OF EXCEPTIONAL ELEMENTS IN CELLULAR MANUFACTURING SYSTEMS USING MULTI-OBJECTIVE GENETIC ALGORITHMS; CHAPTER 22 SINGLE-OBJECTIVE AND MULTI-OBJECTIVE EVOLUTIONARY FLOWSHOP SCHEDULING; CHAPTER 23 EVOLUTIONARY OPERATORS BASED ON ELITE SOLUTIONS FOR BI-OBJECTIVE COMBINATORIAL OPTIMIZATION; CHAPTER 24 MULTI-OBJECTIVE RECTANGULAR PACKING PROBLEM; CHAPTER 25 MULTI-OBJECTIVE ALGORITHMS FOR ATTRIBUTE SELECTION IN DATA MININGCHAPTER 26 FINANCIAL APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS: RECENT DEVELOPMENTS AND FUTURE RESEARCH...; CHAPTER 27 EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION APPROACH TO CONSTRUCTING NEURAL NETWORK ENSEMBLES FOR REGRESSION; CHAPTER 28 OPTIMIZING FORECAST MODEL COMPLEXITY USING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS; CHAPTER 29 EVEN FLOW SCHEDULING PROBLEMS IN FOREST MANAGEMENT; CHAPTER 30 USING DIVERSITY TO GUIDE THE SEARCH IN MULTI-OBJECTIVE OPTIMIZATION; INDEX
• Sommario/riassunto: 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.