Genetic Algorithms for Control and Signal Processing

1. Introduction Background and Biological Inspiration. - 1. 1 Biological Background. - 1. 2 Conventional Genetic Algorithm. - 1. 3 Theory and Hypothesis. - 1. 4 A Simple Example. - 2. Modification in Genetic Algorithm. - 2. 1 Chromosome Representation. - 2. 2 Objective and Fitness Functions. - 2. 3 Selection Methods. - 2. 4 Genetic Operations. - 2. 5 Replacement Scheme. - 3. Intrinsic Characteristics. - 3. 1 Parallel Genetic Algorithm. - 3. 2 Multiple Objective. - 3. 3 Robustness. - 3. 4 Multimodal. - 3. 5 Constraints. - 4. Advanced GA Applications. - 4. 1 Case Study 1: GA in Time Delay Estimation. - 4. 2 Case Study 2: GA in Active Noise Control. - 4. 3 Case Study 3: GA in Automatic Speech Recognition. - 5. Hierarchical Genetic Algorithm. - 5. 1 Biological Inspiration. - 5. 2 Hierarchical Chromosome Formulation. - 5. 3 Genetic Operations. - 5. 4 Multiple Objective Approach. - 6. Filtering Optimization. - 6. 1 Digital IIR Filter Design. - 6. 2 H-infinity Controller Design. - 7. Emerging Technology. - 7. 1 Neural Networks. - 7. 2 Fuzzy Logic. - Appendix A. - Appendix B. - Appendix C. - Appendix D. - Appendix E. - Appendix F. - References. Language: English