Genetic Algorithms for Control and Signal Processing

1. Introduction, Background and Biological Inspiration.- 1.1 Biological Background.- 1.1.1 Coding of DNA.- 1.1.2 Flow of Genetic Information.- 1.1.3 Recombination.- 1.1.4 Mutation.- 1.2 Conventional Genetic Algorithm.- 1.3 Theory and Hypothesis.- 1.3.1 Schema Theory.- 1.3.2 Building Block Hypothesis.- 1.4 A Simple Example.- 2. Modification in Genetic Algorithm.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.2.1 Linear Scaling.- 2.2.2 Sigma Truncation.- 2.2.3 Power Law Scaling.- 2.2.4 Ranking.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.4.1 Crossover.- 2.4.2 Mutation.- 2.4.3 Operational Rates Settings.- 2.4.4 Reordering.- 2.5 Replacement Scheme.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.1.1 Global GA.- 3.1.2 Migration GA.- 3.1.3 Diffusion GA.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 3.5.1 Searching Domain.- 3.5.2 Repair Mechanism.- 3.5.3 Penalty Scheme.- 3.5.4 Specialized Genetic Operations.- 4. Advanced GA Applications.- 4.1 Case Study 1: GA in Time Delay Estimation.- 4.1.1 Problem Formulation.- 4.1.2 Genetic Approach.- 4.1.3 Results.- 4.2 Case Study 2: GA in Active Noise Control.- 4.2.1 Problem Formulation.- 4.2.2 Simple Genetic Algorithm.- 4.2.3 Multiobjective Genetic Algorithm Approach.- 4.2.4 Parallel Genetic Algorithm Approach.- 4.2.5 Hardware GA Processor.- 4.3 Case Study 3: GA in Automatic Speech Recognition.- 4.3.1 Warping Path.- 4.3.2 Implementation of Genetic Time Warping.- 4.3.3 Performance Evaluation.- 5. Hierarchical Genetic Algorithm.- 5.1 Biological Inspiration.- 5.1.1 Regulatory Sequences and Structural Genes.- 5.1.2 Active and Inactive Genes.- 5.2 Hierarchical Chromosome Formulation.- 5.3 Genetic Operations.- 5.4 Multiple Objective Approach.- 5.4.1 Iterative Approach.- 5.4.2 Group Technique.- 5.4.3 Multiple-Objective Ranking.- 6. Filtering Optimization.- 6.1 Digital IIR Filter Design.- 6.1.1 Chromosome Coding.- 6.1.2 The Lowest Filter Order Criterion.- 6.2 H-infinity Controller Design.- 6.2.1 A Mixed Optimization Design Approach.- 6.2.2 Hierarchical Genetic Algorithm.- 6.2.3 The Distillation Column Design.- 6.2.4 Design Comments.- 7. Emerging Technology.- 7.1 Neural Networks.- 7.1.1 Introduction of Neural Network.- 7.1.2 HGA Trained Neural Network (HGANN).- 7.1.3 Simulation Results.- 7.1.4 Application of HGANN on Classification.- 7.2 Fuzzy Logic.- 7.2.1 Basic Formulation of Fuzzy Logic Controller.- 7.2.2 Hierarchical Structure.- 7.2.3 Experimental Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.