Genetic Algorithms for Control and Signal Processing
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This volume provides comprehensive coverage of the emerging field of Genetic Algorithms and their application in solving engineering problems in control and signal processing. The basic genetic operations such as crossover, mutation and reinsertion are discussed, and the characteristics of genetic algorithms, their advantages and constraints, are also described. These processes are illustrated by real-world applications, including a report on the use of genetic algorithms for active noise control. The book closes with a description of a newly proposed and unique hierarchical genetic algorithm ...
This volume provides comprehensive coverage of the emerging field of Genetic Algorithms and their application in solving engineering problems in control and signal processing. The basic genetic operations such as crossover, mutation and reinsertion are discussed, and the characteristics of genetic algorithms, their advantages and constraints, are also described. These processes are illustrated by real-world applications, including a report on the use of genetic algorithms for active noise control. The book closes with a description of a newly proposed and unique hierarchical genetic algorithm designed to address the problems in determining system topology. The use of this formulation in digital filter design is discussed, and the idea is then extended to address neural network optimisation and the construction of a reduced fuzzy membership set and rules for control and signal processing.