The practical application of genetic algorithms to the solution of engineering problems, has rapidly become an established approach in the fields of control and signal processing. Genetic Algorithms provides comprehensive coverage of the techniques involved, describing the intrinsic characteristics, advantages and constraints of genetic algorithms, as well as discussing genetic operations such as crossover, mutation and reinsertion. In addition, the principle of multiobjective optimization and computing parallelism are discussed. The use of genetic algorithms in many areas of interest in control and signal processing is detailed; among the areas of application are:

. filtering;

. H-infinity control;

. speech recognition;

. production planning and scheduling;

. computational intelligence; and

. communication systems.

Also described is an original hierarchical genetic algorithm designed to address the problems in determining system topology.

The authors provide "A Game of Genetic Creatures", a fundamental study for GA based on computer-generated insects to demonstrate some of the ideas developed in the text as a download available from www.springer.com/1-85233-072-4.

This superb book is suitable for readers from a wide range of disciplines.

Assembly Automation

This is a well-written engineering textbook. Genetic algorithms are properly explained and well motivated. The engineering examples illustrate the power of application of genetic algorithms.

Journal of the American Statistical Association

The book is a good contribution to the genetic algorithm area from an applied point of view. It should be read by engineers, undergraduate or postgraduate students and researchers.

International Journal of Adaptive Control and Signal Processing

Klappentext

This comprehensive book gives a overview of the latest discussions in the application of genetic algorithms to solve engineering problems. Featuring real-world applications and an accompanying disk, giving the reader the opportunity to use an interactive genetic algorithms demonstration program.

Inhalt

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. Modifications to Genetic Algorithms.- 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.- 2.6 A Game of Genetic Creatures.- 2.7 Chromosome Representation.- 2.8 Fitness Function.- 2.9 Genetic Operation.- 2.9.1 Selection Window for Functions and Parameters.- 2.10 Demo and Run.- 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. Hierarchical Genetic Algorithm.- 4.1 Biological Inspiration.- 4.1.1 Regulatory Sequences and Structural Genes.- 4.1.2 Active and Inactive Genes.- 4.2 Hierarchical Chromosome Formulation.- 4.3 Genetic Operations.- 4.4 Multiple Objective Approach.- 4.4.1 Iterative Approach.- 4.4.2 Group Technique.- 4.4.3 Multiple-Objective Ranking.- 5. Genetic Algorithms in Filtering.- 5.1 Digital IIR Filter Design.- 5.1.1 Chromosome Coding.- 5.1.2 The Lowest Filter Order Criterion.- 5.2 Time Delay Estimation.- 5.2.1 Problem Formulation.- 5.2.2 Genetic Approach.- 5.2.3 Results.- 5.3 Active Noise Control.- 5.3.1 Problem Formulation.- 5.3.2 Simple Genetic Algorithm.- 5.3.3 Multiobjective Genetic Algorithm Approach.- 5.3.4 Parallel Genetic Algorithm Approach.- 5.3.5 Hardware GA Processor.- 6. Genetic Algorithms in H-infinity Control.- 6.1 A Mixed Optimization Design Approach.- 6.1.1 Hierarchical Genetic Algorithm.- 6.1.2 Application I: The Distillation Column Design.- 6.1.3 Application II: Benchmark Problem.- 6.1.4 Design Comments.- 7. Hierarchical Genetic Algorithms in Computational Intelligence.- 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 Application I: Water Pump System.- 7.2.4 Application II: Solar Plant.- 8. Genetic Algorithms in Speech Recognition Systems.- 8.1 Background of Speech Recognition Systems.- 8.2 Block Diagram of a Speech Recognition System.- 8.3 Dynamic Time Warping.- 8.4 Genetic Time Warping Algorithm (GTW).- 8.4.1 Encoding mechanism.- 8.4.2 Fitness function.- 8.4.3 Selection.- 8.4.4 Crossover.- 8.4.5 Mutation.- 8.4.6 Genetic Time Warping with Relaxed Slope Weighting Function (GTW-RSW).- 8.4.7 Hybrid Genetic Algorithm.- 8.4.8 Performance Evaluation.- 8.5 Hidden Markov Model using Genetic Algorithms.- 8.5.1 Hidden Markov Model.- 8.5.2 Training Discrete HMMs using Genetic Algorithms.- 8.5.3 Genetic Algorithm for Continuous HMM Training.- 8.6 A Multiprocessor System for Parallel Genetic Algorithms.- 8.6.1 Implementation.- 8.7 Global GA for Parallel GA-DTW and PGA-HMM.- 8.7.1 Experimental Results of Nonlinear Time-Normalization by the Parallel GA-DTW.- 8.8 Summary.- 9. Genetic Algorithms in Production Planning and Scheduling Problems.- 9.1 Background of Manufacturing Systems.- 9.2 ETPSP Scheme.- 9.2.1 ETPSP Model.- 9.2.2 Bottleneck Analysis.- 9.2.3 Selection of Key-Processes.- 9.3 Chromosome Configuration.- 9.3.1 Operational Parameters for GA Cycles.- 9.4 GA Application for ETPSP.- 9.4.1 Case 1: Two-product ETPSP.- 9.4.2 Case 2: Multi-product ETPSP.- 9.4.3 Case 3: MOGA Approach.- 9.5 Concluding Remarks.- 10. Genetic Algorithms in Communication Systems.- 10.1 Virtual Path Design in ATM.- 10.1.1 Problem Formulation.- 10.1.2 Average packet delay.- 10.1.3 Constraints.- 10.1.4 Combination Approach.- 10.1.5 Implementation.- 10.1.6 Results.- 10.2 Mesh Communication Network Design.- 10.2.1 Design of Mesh Communication Networks.- 10.2.2 Network Optimization using GA.- 10.2.3 Implementation.- 10.2.4 Results.- 10.3 Wireles Local Area Network Design.- 10.3.1 Problem Formulation.- 10.3.2 Multiobjective HGA Approach.- 10.3.3 Implementation.- 10.3.4 Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.