Genetic algorithms. Concepts and designs
The book offers biologically-driven models of genetic algorithms (GAs) and demonstrates their application to various engineering areas. Chapter 1 introduces the biological background with the three basic operations: selection, recombination and mutation. A simple GA model is explained and the schemata theory and building block hypothesis are sketched. Chapter 2 displays the variety of possible GA designs due to the diversity of encoding methods and genetic operator implementations. A simulation GA game is provided on a diskette and explained in the text. The assets of GAs are discussed in Chapter 3. GAs are praised for their intrinsic parallelism. Three strategies for parallel CA implementation are introduced: global, migration and diffusion. Emphasized also are the GA robustness and the GA abilities to handle multiple objectives and functionally intractable constraints. A hierarchical GA model is introduced in Chapter 4. It mimics the the biological original at a higher level of detail and draws a parallel with the topology of some engineering systems. Genes' active/inactive state and the control subordination underlies the proposed hierarchical GA model. Chapter 5 shows a GA application to filtering. Using the GA models from the previous four chapters, the authors offer solutions for digital IIR filter design, time delay estimation and active noise control. H-infinity control is discussed in Chapter 6. Control system design is regarded as a constrained optimization problem and the solution is sought by a hierarchical GA. The approach is illustrated with two application studies: a distillation column design and a benchmark problem from control theory. Chapter 7 shows how GAs can be used to train multi-layer perceptron (MLP) neural networks (NN) and fuzzy set controllers. In both cases, hierarchical GAs is used. Experimental results of training the MLP NN with the proposed GA are shown. The fuzzy controller design using GAs is exemplified on a water pump system and a solar plant control system. Speech recognition is considered in Chapter 8 focusing on two classical approaches: Dynamic Time Wrapping (DTW) and Hidden Markov Model (HMM), amalgamated with GAs. The advantages of the GA-based DTW and HMM are demonstrated through examples. Detailed is a parallel implementation of the proposed GA-based methods for speech recognition using a multiprocessor system from a hardware and an algorithmic perspective. Chapter 9 describes GAs application to the multi-dimensional and multi-constraint problems of production planning and scheduling. GA solutions of three tasks from communication system design are proposed in Chapter 10: virtual path design in asynchronous transfer mode (ATM), mesh communication network design, and wireless local area network design. Throughout the book the authors use simple but sufficient mathematical formalism, numerous illustrations and tables with experimental results.
scientific article; zbMATH DE number 1330030