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 -- 1.7.4. Part IV: Miscellaneous Applications -- 1.8. 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 -- 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 -- 4.4.3. Injection and Termination.

4.5. 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 -- CHAPTER 6 APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN AUTONOMOUS VEHICLES NAVIGATION -- 6.1. 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 -- 6.4.1. Evaluation of Functions -- 6.4.2. Search Methods -- 6.5. Application of Parameter Identification of an Autonomous Vehicle -- 6.6. Conclusions -- 6.7. Acknowledgement -- References -- CHAPTER 7 AUTOMATING CONTROL SYSTEM DESIGN VIA A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM -- 7.1. Introduction -- 7.2. Performance Based Design Unification and Automation -- 7.2.1. The Overall Design Architecture -- 7.2.2. Control System Formulation -- 7.2.3. Performance Specifications -- 7.3. An Evolutionary ULTIC Design Application -- 7.4. Conclusions -- References -- CHAPTER 8 THE USE OF EVOLUTIONARY ALGORITHMS TO SOLVE PRACTICAL PROBLEMS IN POLYMER EXTRUSION -- 8.1. Introduction -- 8.2. Polymer Extrusion -- 8.2.1. Single Screw Extrusion.

8.2.2. Co-Rotating Twin-Screw Extrusion -- 8.2.3. Optimization Characteristics -- 8.3. Optimization Algorithm -- 8.3.1. Multi-Objective Optimization -- 8.3.2. Reduced Pareto Set Genetic Algorithm with Elitism (RPSGAe) -- 8.3.3. Travelling Salesman Problem -- 8.4. Results and Discussion -- 8.4.1. Single Screw Extrusion -- 8.4.2. Twin-Screw Extrusion -- 8.5. Conclusions -- Acknowledgments -- References -- CHAPTER 9 EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION OF TRUSSES -- 9.1. Introduction -- 9.2. Related Work -- 9.3. ISPAES Algorithm -- 9.3.1. Inverted "ownership" -- 9.3.2. Shrinking the Objective Space -- 9.4. Optimization Examples -- 9.4.1. Optimization of a 49-bar Plane Truss -- 9.4.2. Optimization of a 10-bar Plane Truss -- 9.4.3. Optimization of a 72-bar 3D Structure -- 9.5. Final Remarks and Future Work -- Acknowledgments -- References -- CHAPTER 10 CITY AND REGIONAL PLANNING VIA A MOEA: LESSONS LEARNED -- 10.1. The Traditional Approach -- 10.2. The MOEA Approach -- 10.3. City Planning: Provo and Orem -- 10.4. Regional Planning: The WFMR -- 10.5. Coordinating Regional and City Planning -- 10.6. Conclusions -- Acknowledgments -- References -- CHAPTER 11 A MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR THE COVERING TOUR PROBLEM -- 11.1. Introduction -- 11.2. The Covering Tour Problem -- 11.2.1. The Mono-Objective Covering Tour Problem -- 11.2.2. The Bi-Objective Covering Tour Problem -- 11.2.3. Optimization Methods -- 11.3. A Multi-Objective Evolutionary Algorithm for the Bi-Objective Covering Tour Problem -- 11.3.1. General Framework -- 11.3.2. Solution Coding -- 11.3.3. Genetic Operators -- 11.4. Computational Results -- 11.5. Conclusions and Outlooks -- Acknowledgement -- References -- CHAPTER 12 A COMPUTER ENGINEERING BENCHMARK APPLICATION FOR MULTIOBJECTIVE OPTIMIZERS -- 12.1. Introduction -- 12.2. Packet Processor Design.

12.2.1. Design Space Exploration -- 12.2.2. Basic Models and Methods -- 12.3. Software Architecture -- 12.3.1. General Considerations -- 12.3.2. Interface Description -- 12.4. Test Cases -- 12.4.1. Problem Instances -- 12.4.2. Simulation Results -- 12.5. Summary -- Acknowledgments -- References -- CHAPTER 13 MULTIOBJECTIVE AERODYNAMIC DESIGN AND VISUALIZATION OF SUPERSONIC WINGS BY USING ADAPTIVE RANGE MULTIOBJECTIVE… -- 13.1. Introduction -- 13.2. Adaptive Range Multiobjective Genetic Algorithms -- 13.3. Multiobjective Aerodynamic Optimization -- 13.3.1. Furmulation of Optimization -- 13.3.2. CFD Evaluation -- 13.3.3. Overview of Non-Dominated Solutions -- 13.4. Data Mining by Self-Organizing Map -- 13.4.1. Neural Network and SOM -- 13.4.2. Cluster Analysis -- 13.4.3. Visualization of Design Tradeoffs: SOM of Tradeoffs -- 13.4.4. Data Mining of Design Space: SOM of Design Variables -- 13.5. Conclusions -- Acknowledgments -- References -- CHAPTER 14 APPLICATIONS OF A MULTI-OBJECTIVE GENETIC ALGORITHM IN CHEMICAL AND ENVIRONMENTAL ENGINEERING -- 14.1. Introduction -- 14.2. Physical Problem -- 14.3. Genetic Algorithm -- 14.4. Problem Formulation -- 14.5. Conclusions -- Index -- References -- CHAPTER 15 MULTI-OBJECTIVE SPECTROSCOPIC DATA ANALYSIS OF INERTIAL CONFINEMENT FUSION IMPLOSION CORES: PLASMA GRADIENT… -- 15.1. Introduction -- 15.2. Self-Consistent Analysis of Data from X-ray Images and Line Spectra -- 15.3. A Niched Pareto Genetic Algorithm for Multi-Objective Spectroscopic Data Analysis -- 15.4. Test Cases -- 15.5. Application to Direct-Drive Implosions at GEKKO XII -- 15.6. Application to Indirect-Drive Implosions at OMEGA -- 15.7. Conclusions -- Acknowledgments -- References -- CHAPTER 16 APPLICATION OF MULTIOBJECTIVE EVOLUTIONARY OPTIMIZATION ALGORITHMS IN MEDICINE -- 16.1. Introduction -- 16.2. Medical Image Processing.

16.2.1. Medical Image Reconstruction -- 16.3. Computer Aided Diagnosis -- 16.3.1. Optimization of Diagnostic Classifiers -- 16.3.2. Rules-Based Atrial Disease Diagnosis -- 16.4. Treatment Planning -- 16.4.1. Brachytherapy -- 16.4.2. External Beam Radiotherapy -- 16.4.3. Cancer Chemotherapy -- 16.5. Data Mining -- 16.5.1. Partial Classification -- 16.5.2. Identification of Multiple Gene Subsets -- 16.6. Conclusions -- References -- CHAPTER 17 ON MACHINE LEARNING WITH MULTIOBJECTIVE GENETIC OPTIMIZATION -- 17.1. Introduction -- 17.2. An Overview -- 17.2.1. Machine Learning -- 17.2.2. Generalization -- 17.2.3. Multiobjective Evolutionary Algorithms (MOEA) & Real-World Applications (RWA) -- 17.3. Problem Formulation -- 17.4. MOEA for Partitioning -- 17.4.1. The Algorithm -- 17.4.2. Chromosome Representation -- 17.4.3. Genetic Operators -- 17.4.4. Constraints & Heuristics -- 17.4.5. Convergence -- 17.5. Results and Discussion -- 17.6. Summary & Future Work -- Acknowledgments -- References -- CHAPTER 18 GENERALIZED ANALYSIS OF PROMOTERS: A METHOD FOR DNA SEQUENCE DESCRIPTION -- 18.1. Introduction -- 18.2. Generalized Clustering -- 18.3. Problem: Discovering Promoters in DNA Sequences -- 18.4. Biological Sequence Description Methods -- 18.5. Experimental Algorithm Evaluation -- 18.6. Concluding Remarks -- Appendix -- References -- CHAPTER 19 MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR COMPUTER SCIENCE APPLICATIONS -- 19.1. Introduction -- 19.2. Combinatorial MOP Functions -- 19.3. MOP NPC Examples -- 19.3.1. Multi-Objective Quadratic Assignment Problem -- 19.3.2. MOEA mQAP Results and Analysis -- 19.3.3. Modified Multi-Objective Knapsack Problem (MMOKP) -- 19.3.4. MOEA MMOKP Testing and Analysis -- 19.4. MOEA BB Conjectures for NPC Problems -- 19.5. Future Directions -- References.

CHAPTER 20 DESIGN OF FLUID POWER SYSTEMS USING A MULTI OBJECTIVE GENETIC ALGORITHM.