Genetic algorithms. Concepts and designs
type: Item
instance of: scholarly article
title: Genetic algorithms. Concepts and designs
zbMATH Open document ID: 0926.68113
author: Sam Kwong
author: K. F. Man
author: Wallace K. S. Tang
published in: Advanced Textbooks in Control and Signal Processing
publication date: 1999-09-01T00:00:00Z
review text: 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 …
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68-01
Mathematics Subject Classification ID: 68W10
zbMATH DE Number: 1330030
zbMATH Keywords: genetic algorithms
zbMATH Keywords: engineering applications
zbMATH Keywords: filtering
zbMATH Keywords: speech recognition
zbMATH Keywords: H-infinity control systems
zbMATH Keywords: computational intelligence
zbMATH Keywords: production and planning
zbMATH Keywords: communication systems
reviewed by: Ludmila I. Kuncheva
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1330030