Genetic Algorithms for Control and Signal Processing

This volume provides coverage of the field of genetic algorithms and their applications 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 algoritms for active noise control.

The book closes with a description of a proposed 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 optimization and the construction of a reduced fuzzy membership set and rules for control and signal processing.