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Intro; Preface; Contents; Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms; 1 Introduction; 1.1 Heuristic and Metaheuristic Algorithms; 1.2 Image Segmentation; 1.3 Characteristics of the Remote Sensing Images; 1.4 Satellite Image Types and Sources; 2 Evolutionary Algorithms; 2.1 Genetic Algorithm; 2.2 Hill-Climbing Algorithm; 2.3 Hybrid Genetic Algorithm; 2.4 Example of HyGA Segmentation; 3 Dynamic Genetic Algorithm; 3.1 Structure of the Dynamic Genetic Algorithm; 3.2 Example of Hybrid Dynamic GA (HyDyGA)
4 New Methods of Cooperation Between Metaheuristics and Other Algorithms4.1 Hybrid Genetic Algorithm (HyGA) and Self-Organizing Maps (SOMs); 4.2 Hybrid Dynamic (GA) and Fuzzy C-Means (FCM); 4.3 Examples of Image Segmentation Using SOMs-HyGA; 4.4 Examples of Satellite Image Segmentation Using FCM-HyDyGA; 5 Metaheuristic Performance Analysis; 5.1 Metaheuristic Algorithm Complexity Analysis; 5.2 Robustness and Efficiency Analysis; 5.3 Responsiveness Analysis; 6 Discussion; 7 Conclusion; References; A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation
1 Introduction2 Related Works; 3 Overview of Quantum Computing; 3.1 Definition of a Quantum Bit; 3.2 Quantum Register; 3.3 Quantum Measure; 3.4 Quantum Algorithms; 4 Quantum Genetic Algorithm Principles; 4.1 Coding of Quantum Chromosomes; 4.2 Measuring Chromosomes; 4.3 Quantum Genetic Operations; 5 The Proposed Approach; 5.1 From Cellular Automata to Chromosome; 5.2 Initialization; 5.3 Measure of Quantum Chromosomes; 5.4 Evaluation of Solutions; 5.5 Updating Chromosomes by Interference; 5.6 Updating of Best Solutions; 6 Experimental Results; 7 Comparison Between Quantum GA and Conventional GA
7.1 Visual Results7.2 Numerical Results; 8 Conclusion; References; Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers; 1 Introduction; 2 Feature Extraction; 2.1 Gabor Filters; 2.2 Local Binary Patterns and Orthogonal Combination of Local Binary Patterns; 2.3 Histogram of Oriented Gradients; 3 Distance-Based Classification; 4 Proposed Hybrid Metaheuristic GA-SVM Model for Classification; 4.1 Support Vector Machines; 4.2 Genetic Algorithm; 4.3 Chromosome Design; 4.4 Fitness Function; 4.5 Design of the Proposed GA-SVM Model
5 Proposed Hybrid Face Recognition Approaches5.1 Integrating OC-LBP and HOG Features; 5.2 Gabor Filtered Zernike Moments; 6 Empirical Evaluation; 6.1 Datasets Used; 6.1.1 ORL Database; 6.1.2 Yale Database; 6.1.3 FERET Database; 6.2 Implementation Parameters; 6.3 Database Generation for Validation; 6.4 Performance Evaluation of the Integrated OC-LBP and HOG Approaches; 6.5 Performance Evaluation of the Gabor Filtered ZM Method; 7 Performance Comparison with Other Similar and State-of-the-Art Methods; 8 Performance Evaluation on the Self-generated Database
4 New Methods of Cooperation Between Metaheuristics and Other Algorithms4.1 Hybrid Genetic Algorithm (HyGA) and Self-Organizing Maps (SOMs); 4.2 Hybrid Dynamic (GA) and Fuzzy C-Means (FCM); 4.3 Examples of Image Segmentation Using SOMs-HyGA; 4.4 Examples of Satellite Image Segmentation Using FCM-HyDyGA; 5 Metaheuristic Performance Analysis; 5.1 Metaheuristic Algorithm Complexity Analysis; 5.2 Robustness and Efficiency Analysis; 5.3 Responsiveness Analysis; 6 Discussion; 7 Conclusion; References; A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation
1 Introduction2 Related Works; 3 Overview of Quantum Computing; 3.1 Definition of a Quantum Bit; 3.2 Quantum Register; 3.3 Quantum Measure; 3.4 Quantum Algorithms; 4 Quantum Genetic Algorithm Principles; 4.1 Coding of Quantum Chromosomes; 4.2 Measuring Chromosomes; 4.3 Quantum Genetic Operations; 5 The Proposed Approach; 5.1 From Cellular Automata to Chromosome; 5.2 Initialization; 5.3 Measure of Quantum Chromosomes; 5.4 Evaluation of Solutions; 5.5 Updating Chromosomes by Interference; 5.6 Updating of Best Solutions; 6 Experimental Results; 7 Comparison Between Quantum GA and Conventional GA
7.1 Visual Results7.2 Numerical Results; 8 Conclusion; References; Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers; 1 Introduction; 2 Feature Extraction; 2.1 Gabor Filters; 2.2 Local Binary Patterns and Orthogonal Combination of Local Binary Patterns; 2.3 Histogram of Oriented Gradients; 3 Distance-Based Classification; 4 Proposed Hybrid Metaheuristic GA-SVM Model for Classification; 4.1 Support Vector Machines; 4.2 Genetic Algorithm; 4.3 Chromosome Design; 4.4 Fitness Function; 4.5 Design of the Proposed GA-SVM Model
5 Proposed Hybrid Face Recognition Approaches5.1 Integrating OC-LBP and HOG Features; 5.2 Gabor Filtered Zernike Moments; 6 Empirical Evaluation; 6.1 Datasets Used; 6.1.1 ORL Database; 6.1.2 Yale Database; 6.1.3 FERET Database; 6.2 Implementation Parameters; 6.3 Database Generation for Validation; 6.4 Performance Evaluation of the Integrated OC-LBP and HOG Approaches; 6.5 Performance Evaluation of the Gabor Filtered ZM Method; 7 Performance Comparison with Other Similar and State-of-the-Art Methods; 8 Performance Evaluation on the Self-generated Database