Hybrid evolutionary algorithms: with ... 88 tables
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2007
|
| Series: | Studies in computational intelligence
75 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XV, 403 S. Ill., graph. Darst. |
| ISBN: | 9783540732969 |
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| 245 | 1 | 0 | |a Hybrid evolutionary algorithms |b with ... 88 tables |c Grosan, Crina ... (eds.) |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2007 | |
| 300 | |a XV, 403 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence |v 75 | |
| 650 | 4 | |a Evolutionary programming (Computer science) | |
| 650 | 4 | |a Evolutionary computation | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
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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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| illustrated | Illustrated |
| index_date | 2024-07-02T22:59:43Z |
| indexdate | 2024-07-09T21:26:58Z |
| institution | BVB |
| isbn | 9783540732969 |
| language | English |
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| physical | XV, 403 S. Ill., graph. Darst. |
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| 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 |
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