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Hybrid evolutionary algorithms: with ... 88 tables

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Bibliographische Detailangaben
Format:	Buch
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 2007
Schriftenreihe:	Studies in computational intelligence 75
Schlagworte:	Evolutionary programming (Computer science) Evolutionary computation Genetic algorithms Evolutionärer Algorithmus
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XV, 403 S. Ill., graph. Darst.
ISBN:	9783540732969

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Datensatz im Suchindex

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adam_text	Contents 1 Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews C. Grosan and A. Abraham .......................................... 1 1.1 Introduction .................................................. 1 1.2 Architectures of Hybrid Evolutionary Algorithms ................... 4 1.3 Hybrid Evolutionary Architectures ............................... 5 1.3.1 Evolutionary Algorithms Assisted by Evolutionary Algorithms ............................................ 6 1.3.2 Evolutionary Algorithms Assisted by Neural Networks ....... 6 1.3.3 Fuzzy Logic Assisted Evolutionary Algorithms .............. 7 1.3.4 Evolutionary Algorithms Assisted by Particle Swarm Optimization .......................................... 8 1.3.5 Evolutionary Algorithms Assisted by Ant Colony Optimization 9 1.3.6 Evolutionary Algorithms Assisted by Bacterial Foraging ...... 9 1.3.7 Evolutionary Algorithms Incorporating Prior Knowledge ..... 10 1.3.8 Hybrid Approaches Incorporating Local Search and Others ... 10 1.4 Conclusions .................................................. 13 References ......................................................... 13 2 Quantum-Inspired Evolutionary Algorithm for Numerical Optimization A.V. Abs da Cruz, M.M.B.R. Vellasco, and M.A.C. Pacheco ................ 19 2.1 Introduction .................................................. 19 2.2 The Quantum-Inspired Evolutionary Algorithm using a Real Number Representation .............................. 20 2.2.1 The Quantum Population ................................ 20 2.2.2 Quantum Individuals Observation ........................ 22 2.2.3 Updating the Quantum Population ........................ 27 2.3 Case Studies .................................................. 27 2.3.1 Optimization of Benchmark Functions .................... 28 2.3.2 Discussion ............................................ 31 2.3.3 Supervised Learning Problems ........................... 35 X Contents 2.4 Conclusions and Future Works ................................... 36 2.5 Acknowledgments............................................. 37 References ......................................................... 37 3 Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective G. Dellino, P. Lino, С Meloni, and A. Rizzo ............................ 39 3.1 Introduction .................................................. 39 3.2 Multidisciplinary Design Optimization: A Control Engineering Perspective ............................... 40 3.3 An Enhanced Evolutionary Scheme for Design Optimization ......... 44 3.3.1 The Multiobjective Optimizer ............................ 45 3.3.2 The Solutions Archive .................................. 48 3.3.3 The Solutions Analyzer ................................. 48 3.3.4 Enhancing the Algorithm with Metamodels ................. 50 3.4 Case Study: Optimal Design of a Pressure Controller of a CNG Injection System ...................................... 55 3.4.1 The CNG Injection System and the Injection Pressure Controller ............................................. 56 3.4.2 Mechanical and Control Design Optimization Issues ......... 57 3.4.3 Problem Formulation ................................... 57 3.4.4 The Setting of the Algorithm ............................. 62 3.4.5 Computational Results .................................. 63 3.5 Conclusion ................................................... 73 3.6 Acknowledgments ............................................. 74 References ......................................................... 74 4 Hybrid Evolutionary Algorithms and Clustering Search A.C.M. Oliveira and LA.N. Lorena ................................... 77 4.1 Introduction .................................................. 77 4.2 Related Works ................................................ 78 4.3 Clustering Search Foundations ................................... 80 4.3.1 Components ........................................... 81 4.3.2 The Clustering Process .................................. 82 4.3.3 Assimilation ........................................... 83 4.4 ECS for Unconstrained Continuous Optimization ................... 85 4.4.1 Implementation ........................................ 85 4.4.2 Results ............................................... 86 4.5 ECS for Pattern Sequencing ..................................... 87 4.5.1 Theoretical Issues of the GMLP .......................... 88 4.5.2 Implementation ........................................ 89 4.5.3 Computational Experiments .............................. 91 4.6 Conclusion ................................................... 97 References ......................................................... 98 Contents XI 5 A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy W. Mo, S.-U. Guan, and Sadasivan Puthusserypady К .................... 101 5.1 Introduction .................................................. 101 5.1.1 Background ........................................... 101 5.1.2 Challenges and Proposed Solution ........................ 103 5.2 Related Work ................................................. 105 5.2.1 Motivation ............................................ 105 5.2.2 Cutting Plane Mechanism: Local via Global Search .......... 106 5.2.3 Summary ............................................. 108 5.3 Particle Swarm Assisted Incremental Evolution Strategy (PIES) ....... 108 5.3.1 Architecture and Procedure of PIES ....................... 108 5.3.2 Implementation of SVE and MVE ......................... 109 5.3.3 Operation of Integration ................................. Ill 5.4 Experiments and Results ........................................ 112 5.4.1 Performance Evaluation Metrics .......................... 112 5.4.2 Experiment Scheme .................................... 112 5.4.3 Experiment Results ..................................... 113 5.4.4 Analysis of Experiment Results ........................... 119 5.5 Discussion ................................................... 122 5.6 Conclusions .................................................. 123 References ......................................................... 124 6 An Efficient Nearest Neighbor Classifier R. Frédéric and G. Serge ........................................... 127 6.1 Introduction .................................................. 127 6.2 Problem Statement ............................................. 129 6.3 The Hybrid Algorithm .......................................... 130 6.3.1 The Genetic Algorithm .................................. 131 6.3.2 Local Tuning .......................................... 134 6.4 Results and Discussion ......................................... 136 6.4.1 The Data Used ......................................... 136 6.4.2 Comparison with Known Algorithms ...................... 137 6.4.3 Main Results .......................................... 138 6.4.4 Complementary Results ................................. 141 6.5 Conclusion ................................................... 143 References ......................................................... 143 7 Hybrid Genetic: Particle Swarm Optimization Algorithm D.H. Kim, A. Abraham, and K. Hirota ................................. 147 7.1 Introduction .................................................. 147 7.2 Hybrid Approach Using Euclidean Distance Genetic Algorithm and Particle Swarm Optimization Algorithm ....................... 148 7.2.1 Particle Swarm Optimization Algorithm .................... 148 7.2.2 Genetic Algorithm with Euclidean Data Distance ............ 149 ХП Contents 7.3 Experiment Results ............................................ 153 7.3.1 Performance Analysis for Different Particle Sizes ............ 153 7.3.2 Performance Characteristics of Hybrid GA-PSO Algorithm ... 155 7.3.3 Importance of GA Parameter Selection ..................... 155 7.4 PID Controller Tuning for the AVR System ........................ 162 7.5 Conclusions .................................................. 168 References ......................................................... 170 8 A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection D.H. Kim and A. Abraham .......................................... 171 8.1 Introduction .................................................. 171 8.2 Hybrid System Consisting of Genetic Algorithm and Bacteria Foraging .......................................... 172 8.2.1 Genetic Algorithms ..................................... 172 8.2.2 Bacterial Foraging Algorithm ............................ 173 8.3 Experiment Results Using Test Functions .......................... 176 8.3.1 Mutation Operation in GA-BF ........................... 176 8.3.2 Crossover Operation in GA-BF ........................... 177 8.3.3 Performance Variation for Different Step Sizes .............. 178 8.3.4 Performance for Different Chemotactic Steps of GA-BF ...... 179 8.3.5 Performance for Different Life Time (Ns) ................... 180 8.3.6 Performance of GA-BF for Test Functions ................. 180 8.4 Intelligent Tuning of PID Controller for Automatic Voltage Regulator (AVR) Using GA-BF Approach ................................. 184 8.5 PID Controller Tuning With Disturbance Rejection Function ......... 191 8.5.1 Condition for Disturbance Rejection ....................... 191 8.5.2 Performance Index for Disturbance Rejection Controller Design ................................................ 195 8.5.3 Simulations and Discussions ............................. 197 8.6 Conclusions .................................................. 198 8.7 Acknowledgements ............................................ 198 References ......................................................... 198 9 Memetic Algorithms Parametric Optimization for Microlithography С Dürr, T. Fühner, В. Tollkühn, Α. Erdmann, and G. Kókai ................ 201 9.1 Introduction .................................................. 201 9.2 Optical Microlithography ....................................... 203 9.2.1 Simulation of Optical Microlithography .................... 203 9.3 Memetic Algorithms ........................................... 206 9.3.1 Background on Memetic Algorithms ...................... 206 9.3.2 A Memetic Algorithm Using a Genetic Algorithm and SQP Local Search .................................. 211 9.4 Experiments .................................................. 223 Contents ХШ 9.4.1 Benchmark Functions ................................... 223 9.4.2 Simulation of the Efficient Resist Model Parametric Optimization Problem ................................... 233 9.5 Conclusions and Future Work ................................... 236 9.6 Acknowledgments ............................................. 237 References ......................................................... 238 10 Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction Md.T. Hoque, M. Chetty, andLS. Dooley ..............................241 10.1 Introduction .................................................. 241 10.2 Background: The Protein Folding ................................ 242 10.2.1 Inner Structure of Proteins ............................... 242 10.2.2 The Search Problem .................................... 243 10.2.3 Importance of the Protein Folding ......................... 245 10.2.4 Available Prediction Technologies ......................... 246 10.3 Computational Approaches ...................................... 246 10.3.1 Molecular Dynamics .................................... 247 10.3.2 Model-Based Approaches ................................ 248 10.4 Conclusions .................................................. 265 References ......................................................... 265 11 A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids F. Xhafa .........................................................269 11.1 Introduction .................................................. 269 11.2 Computational Grids ........................................... 271 11.3 Job Scheduling on Computational Grids ........................... 273 11.4 Related Work ................................................. 278 11.5 Memetic Algorithm for Job Scheduling on Computational Grids ...... 278 11.5.1 Outline of MA for Job Scheduling ........................ 279 11.6 Local Search Procedures ........................................ 283 11.6.1 Neighborhood Exploration ............................... 284 11.6.2 Tabu search: Local Tabu Hop ............................. 289 11.6.3 Movement Evaluation ................................... 291 11.6.4 Optimization Criterion of Local Search .................... 292 11.7 Implementation Issues .......................................... 293 11.8 Experimental Study ............................................ 294 11.8.1 Fine Tuning ........................................... 295 11.8.2 Computational Results: Evaluation of MA and MA + TS ...... 303 11.9 Job Scheduling in a Dynamic Setting ............................. 305 11.10 Conclusions and Further Work ................................... 308 11.11 Acknowledgement ............................................. 309 References ......................................................... 309 XIV Contents 12 Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between ¿-Means and Evolutionary Search E.R. Hruschka, L.N. de Castro, and R.J.G.B. Campello ....................313 12.1 Introduction .................................................. 313 12.2 Evolutionary Algorithms For Gene-Expression Data Analysis: A Brief Review ............................................... 315 12.2.1 Gene Selection ......................................... 316 12.2.2 Gene Clustering ........................................ 316 12.2.3 Gene Ordering and Other Key Applications ................. 317 12.3 Clustering Problem ............................................ 317 12.3.1 Similarity and Dissimilarity Measures ..................... 317 12.3.2 Partitioning Approach ................................... 318 12.4 Evolutionary Algorithm to Optimize fc-Means ...................... 319 12.4.1 The ¿-Means Clustering Algorithm ........................ 319 12.4.2 Evolutionary Algorithm for Clustering ..................... 320 12.5 Results ....................................................... 324 12.6 Conclusions and Future Work ................................... 329 12.7 Acknowledgements ............................................ 330 References ......................................................... 330 13 Robust Parametric Image Registration F. Calderón, J.J. Flores, and L. Romero ................................337 13.1 Introduction .................................................. 337 13.2 Registration using an Affine Transformation ....................... 339 13.3 Outliers and Parameter Estimation ................................ 340 13.3.1 Robust Statistical Estimation ............................. 340 13.3.2 RANSAC for Image Registration ......................... 341 13.3.3 SSD-ARC ............................................ 342 13.4 Hybrid Genetic/Gradient-Based Optimization ...................... 344 13.4.1 Genetic Algorithms ..................................... 344 13.4.2 SSD-ARC Minimization by NLM ........................ 345 13.5 Results ....................................................... 347 13.5.1 Synthetic Images ....................................... 348 13.5.2 Image Registration with Real Images ...................... 353 13.6 Conclusions .................................................. 359 References ......................................................... 359 14 Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP R. Kumar andP.K. Singh ........................................... 361 14.1 Introduction .................................................. 361 14.2 Problem Formulation ........................................... 362 14.3 Single Objective TSP: A Review ................................. 364 14.3.1 TSP Heuristics ......................................... 364 14.3.2 Approximation Algorithms .............................. 369 Contents XV 14.3.3 Tabu Search-Based Algorithms ........................... 370 14.3.4 Simulated Annealing for TSP ............................ 372 14.3.5 Neural Networks for TSP ................................ 373 14.3.6 Genetic/Evolutionary Algorithms for TSP .................. 374 14.4 Multiobjective TSP ............................................ 375 14.4.1 Multiobjective Optimization: Preliminaries ................. 375 14.4.2 Issues In Multiobjective Optimization ..................... 377 14.4.3 Hybrid Evolutionary Multiobjective Optimizers ............. 378 14.4.4 Hybrid Solutions for TSP: Previous Work .................. 380 14.5 Hybrid EA with Local Search ................................... 382 14.5.1 Algorithm ............................................. 382 14.5.2 Assessing Convergence with Rank-Histograms .............. 383 14.5.3 Results ............................................... 384 14.5.4 EA Hybridized with Local Search ......................... 386 14.5.5 Improved Results with Hybridization ...................... 388 14.6 Conclusions .................................................. 393 References ......................................................... 394 Index ...........................................................399
adam_txt	Contents 1 Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews C. Grosan and A. Abraham . 1 1.1 Introduction . 1 1.2 Architectures of Hybrid Evolutionary Algorithms . 4 1.3 Hybrid Evolutionary Architectures . 5 1.3.1 Evolutionary Algorithms Assisted by Evolutionary Algorithms . 6 1.3.2 Evolutionary Algorithms Assisted by Neural Networks . 6 1.3.3 Fuzzy Logic Assisted Evolutionary Algorithms . 7 1.3.4 Evolutionary Algorithms Assisted by Particle Swarm Optimization . 8 1.3.5 Evolutionary Algorithms Assisted by Ant Colony Optimization 9 1.3.6 Evolutionary Algorithms Assisted by Bacterial Foraging . 9 1.3.7 Evolutionary Algorithms Incorporating Prior Knowledge . 10 1.3.8 Hybrid Approaches Incorporating Local Search and Others . 10 1.4 Conclusions . 13 References . 13 2 Quantum-Inspired Evolutionary Algorithm for Numerical Optimization A.V. Abs da Cruz, M.M.B.R. Vellasco, and M.A.C. Pacheco . 19 2.1 Introduction . 19 2.2 The Quantum-Inspired Evolutionary Algorithm using a Real Number Representation . 20 2.2.1 The Quantum Population . 20 2.2.2 Quantum Individuals Observation . 22 2.2.3 Updating the Quantum Population . 27 2.3 Case Studies . 27 2.3.1 Optimization of Benchmark Functions . 28 2.3.2 Discussion . 31 2.3.3 Supervised Learning Problems . 35 X Contents 2.4 Conclusions and Future Works . 36 2.5 Acknowledgments. 37 References . 37 3 Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective G. Dellino, P. Lino, С Meloni, and A. Rizzo . 39 3.1 Introduction . 39 3.2 Multidisciplinary Design Optimization: A Control Engineering Perspective . 40 3.3 An Enhanced Evolutionary Scheme for Design Optimization . 44 3.3.1 The Multiobjective Optimizer . 45 3.3.2 The Solutions Archive . 48 3.3.3 The Solutions Analyzer . 48 3.3.4 Enhancing the Algorithm with Metamodels . 50 3.4 Case Study: Optimal Design of a Pressure Controller of a CNG Injection System . 55 3.4.1 The CNG Injection System and the Injection Pressure Controller . 56 3.4.2 Mechanical and Control Design Optimization Issues . 57 3.4.3 Problem Formulation . 57 3.4.4 The Setting of the Algorithm . 62 3.4.5 Computational Results . 63 3.5 Conclusion . 73 3.6 Acknowledgments . 74 References . 74 4 Hybrid Evolutionary Algorithms and Clustering Search A.C.M. Oliveira and LA.N. Lorena . 77 4.1 Introduction . 77 4.2 Related Works . 78 4.3 Clustering Search Foundations . 80 4.3.1 Components . 81 4.3.2 The Clustering Process . 82 4.3.3 Assimilation . 83 4.4 ECS for Unconstrained Continuous Optimization . 85 4.4.1 Implementation . 85 4.4.2 Results . 86 4.5 ECS for Pattern Sequencing . 87 4.5.1 Theoretical Issues of the GMLP . 88 4.5.2 Implementation . 89 4.5.3 Computational Experiments . 91 4.6 Conclusion . 97 References . 98 Contents XI 5 A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy W. Mo, S.-U. Guan, and Sadasivan Puthusserypady К . 101 5.1 Introduction . 101 5.1.1 Background . 101 5.1.2 Challenges and Proposed Solution . 103 5.2 Related Work . 105 5.2.1 Motivation . 105 5.2.2 Cutting Plane Mechanism: Local via Global Search . 106 5.2.3 Summary . 108 5.3 Particle Swarm Assisted Incremental Evolution Strategy (PIES) . 108 5.3.1 Architecture and Procedure of PIES . 108 5.3.2 Implementation of SVE and MVE . 109 5.3.3 Operation of Integration . Ill 5.4 Experiments and Results . 112 5.4.1 Performance Evaluation Metrics . 112 5.4.2 Experiment Scheme . 112 5.4.3 Experiment Results . 113 5.4.4 Analysis of Experiment Results . 119 5.5 Discussion . 122 5.6 Conclusions . 123 References . 124 6 An Efficient Nearest Neighbor Classifier R. Frédéric and G. Serge . 127 6.1 Introduction . 127 6.2 Problem Statement . 129 6.3 The Hybrid Algorithm . 130 6.3.1 The Genetic Algorithm . 131 6.3.2 Local Tuning . 134 6.4 Results and Discussion . 136 6.4.1 The Data Used . 136 6.4.2 Comparison with Known Algorithms . 137 6.4.3 Main Results . 138 6.4.4 Complementary Results . 141 6.5 Conclusion . 143 References . 143 7 Hybrid Genetic: Particle Swarm Optimization Algorithm D.H. Kim, A. Abraham, and K. Hirota . 147 7.1 Introduction . 147 7.2 Hybrid Approach Using Euclidean Distance Genetic Algorithm and Particle Swarm Optimization Algorithm . 148 7.2.1 Particle Swarm Optimization Algorithm . 148 7.2.2 Genetic Algorithm with Euclidean Data Distance . 149 ХП Contents 7.3 Experiment Results . 153 7.3.1 Performance Analysis for Different Particle Sizes . 153 7.3.2 Performance Characteristics of Hybrid GA-PSO Algorithm . 155 7.3.3 Importance of GA Parameter Selection . 155 7.4 PID Controller Tuning for the AVR System . 162 7.5 Conclusions . 168 References . 170 8 A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection D.H. Kim and A. Abraham . 171 8.1 Introduction . 171 8.2 Hybrid System Consisting of Genetic Algorithm and Bacteria Foraging . 172 8.2.1 Genetic Algorithms . 172 8.2.2 Bacterial Foraging Algorithm . 173 8.3 Experiment Results Using Test Functions . 176 8.3.1 Mutation Operation in GA-BF . 176 8.3.2 Crossover Operation in GA-BF . 177 8.3.3 Performance Variation for Different Step Sizes . 178 8.3.4 Performance for Different Chemotactic Steps of GA-BF . 179 8.3.5 Performance for Different Life Time (Ns) . 180 8.3.6 Performance of GA-BF for Test Functions . 180 8.4 Intelligent Tuning of PID Controller for Automatic Voltage Regulator (AVR) Using GA-BF Approach . 184 8.5 PID Controller Tuning With Disturbance Rejection Function . 191 8.5.1 Condition for Disturbance Rejection . 191 8.5.2 Performance Index for Disturbance Rejection Controller Design . 195 8.5.3 Simulations and Discussions . 197 8.6 Conclusions . 198 8.7 Acknowledgements . 198 References . 198 9 Memetic Algorithms Parametric Optimization for Microlithography С Dürr, T. Fühner, В. Tollkühn, Α. Erdmann, and G. Kókai . 201 9.1 Introduction . 201 9.2 Optical Microlithography . 203 9.2.1 Simulation of Optical Microlithography . 203 9.3 Memetic Algorithms . 206 9.3.1 Background on Memetic Algorithms . 206 9.3.2 A Memetic Algorithm Using a Genetic Algorithm and SQP Local Search . 211 9.4 Experiments . 223 Contents ХШ 9.4.1 Benchmark Functions . 223 9.4.2 Simulation of the Efficient Resist Model Parametric Optimization Problem . 233 9.5 Conclusions and Future Work . 236 9.6 Acknowledgments . 237 References . 238 10 Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction Md.T. Hoque, M. Chetty, andLS. Dooley .241 10.1 Introduction . 241 10.2 Background: The Protein Folding . 242 10.2.1 Inner Structure of Proteins . 242 10.2.2 The Search Problem . 243 10.2.3 Importance of the Protein Folding . 245 10.2.4 Available Prediction Technologies . 246 10.3 Computational Approaches . 246 10.3.1 Molecular Dynamics . 247 10.3.2 Model-Based Approaches . 248 10.4 Conclusions . 265 References . 265 11 A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids F. Xhafa .269 11.1 Introduction . 269 11.2 Computational Grids . 271 11.3 Job Scheduling on Computational Grids . 273 11.4 Related Work . 278 11.5 Memetic Algorithm for Job Scheduling on Computational Grids . 278 11.5.1 Outline of MA for Job Scheduling . 279 11.6 Local Search Procedures . 283 11.6.1 Neighborhood Exploration . 284 11.6.2 Tabu search: Local Tabu Hop . 289 11.6.3 Movement Evaluation . 291 11.6.4 Optimization Criterion of Local Search . 292 11.7 Implementation Issues . 293 11.8 Experimental Study . 294 11.8.1 Fine Tuning . 295 11.8.2 Computational Results: Evaluation of MA and MA + TS . 303 11.9 Job Scheduling in a Dynamic Setting . 305 11.10 Conclusions and Further Work . 308 11.11 Acknowledgement . 309 References . 309 XIV Contents 12 Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between ¿-Means and Evolutionary Search E.R. Hruschka, L.N. de Castro, and R.J.G.B. Campello .313 12.1 Introduction . 313 12.2 Evolutionary Algorithms For Gene-Expression Data Analysis: A Brief Review . 315 12.2.1 Gene Selection . 316 12.2.2 Gene Clustering . 316 12.2.3 Gene Ordering and Other Key Applications . 317 12.3 Clustering Problem . 317 12.3.1 Similarity and Dissimilarity Measures . 317 12.3.2 Partitioning Approach . 318 12.4 Evolutionary Algorithm to Optimize fc-Means . 319 12.4.1 The ¿-Means Clustering Algorithm . 319 12.4.2 Evolutionary Algorithm for Clustering . 320 12.5 Results . 324 12.6 Conclusions and Future Work . 329 12.7 Acknowledgements . 330 References . 330 13 Robust Parametric Image Registration F. Calderón, J.J. Flores, and L. Romero .337 13.1 Introduction . 337 13.2 Registration using an Affine Transformation . 339 13.3 Outliers and Parameter Estimation . 340 13.3.1 Robust Statistical Estimation . 340 13.3.2 RANSAC for Image Registration . 341 13.3.3 SSD-ARC . 342 13.4 Hybrid Genetic/Gradient-Based Optimization . 344 13.4.1 Genetic Algorithms . 344 13.4.2 SSD-ARC Minimization by NLM . 345 13.5 Results . 347 13.5.1 Synthetic Images . 348 13.5.2 Image Registration with Real Images . 353 13.6 Conclusions . 359 References . 359 14 Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP R. Kumar andP.K. Singh . 361 14.1 Introduction . 361 14.2 Problem Formulation . 362 14.3 Single Objective TSP: A Review . 364 14.3.1 TSP Heuristics . 364 14.3.2 Approximation Algorithms . 369 Contents XV 14.3.3 Tabu Search-Based Algorithms . 370 14.3.4 Simulated Annealing for TSP . 372 14.3.5 Neural Networks for TSP . 373 14.3.6 Genetic/Evolutionary Algorithms for TSP . 374 14.4 Multiobjective TSP . 375 14.4.1 Multiobjective Optimization: Preliminaries . 375 14.4.2 Issues In Multiobjective Optimization . 377 14.4.3 Hybrid Evolutionary Multiobjective Optimizers . 378 14.4.4 Hybrid Solutions for TSP: Previous Work . 380 14.5 Hybrid EA with Local Search . 382 14.5.1 Algorithm . 382 14.5.2 Assessing Convergence with Rank-Histograms . 383 14.5.3 Results . 384 14.5.4 EA Hybridized with Local Search . 386 14.5.5 Improved Results with Hybridization . 388 14.6 Conclusions . 393 References . 394 Index .399
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id	DE-604.BV035186053
illustrated	Illustrated
index_date	2024-07-02T22:59:43Z
indexdate	2024-07-09T21:26:58Z
institution	BVB
isbn	9783540732969
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-016992744
oclc_num	634053628
open_access_boolean
owner	DE-703
owner_facet	DE-703
physical	XV, 403 S. Ill., graph. Darst.
publishDate	2007
publishDateSearch	2007
publishDateSort	2007
publisher	Springer
record_format	marc
series	Studies in computational intelligence
series2	Studies in computational intelligence
spelling	Hybrid evolutionary algorithms with ... 88 tables Grosan, Crina ... (eds.) Berlin [u.a.] Springer 2007 XV, 403 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 75 Evolutionary programming (Computer science) Evolutionary computation Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s 1\p DE-604 Grosan, Crina Sonstige oth Studies in computational intelligence 75 (DE-604)BV020822171 75 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016992744&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Hybrid evolutionary algorithms with ... 88 tables Studies in computational intelligence Evolutionary programming (Computer science) Evolutionary computation Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd
subject_GND	(DE-588)4366912-8
title	Hybrid evolutionary algorithms with ... 88 tables
title_auth	Hybrid evolutionary algorithms with ... 88 tables
title_exact_search	Hybrid evolutionary algorithms with ... 88 tables
title_exact_search_txtP	Hybrid evolutionary algorithms with ... 88 tables
title_full	Hybrid evolutionary algorithms with ... 88 tables Grosan, Crina ... (eds.)
title_fullStr	Hybrid evolutionary algorithms with ... 88 tables Grosan, Crina ... (eds.)
title_full_unstemmed	Hybrid evolutionary algorithms with ... 88 tables Grosan, Crina ... (eds.)
title_short	Hybrid evolutionary algorithms
title_sort	hybrid evolutionary algorithms with 88 tables
title_sub	with ... 88 tables
topic	Evolutionary programming (Computer science) Evolutionary computation Genetic algorithms Evolutionärer Algorithmus (DE-588)4366912-8 gnd
topic_facet	Evolutionary programming (Computer science) Evolutionary computation Genetic algorithms Evolutionärer Algorithmus
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016992744&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV020822171
work_keys_str_mv	AT grosancrina hybridevolutionaryalgorithmswith88tables

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