Stability and optimality in genetic algorithm controllers

Genetic algorithms are stochastic search techniques that guide a population of solutions towards an optimum using the principles of evolution and natural genetics. In recent years, genetic algorithms have become a popular optimization tool for many areas of research, including the field of system control and control design. Significant research exists concerning genetic algorithms for control design and off-line controller analyses. However, little work has been done with on-line genetic algorithm controls primarily because of the problems associated with instability in early stages of the controller's evolution. Also, until recently the stability of controllers based on genetic algorithms has not been researched in detail. This study presents a method of adaptive system control based on genetic algorithms. The method consists of a population of controllers evolving towards an optimum controller through the use of probabilistic genetic operators. The scope of the research encompasses an analysis of the stability and optimality of the resulting control system with respect to the convergence of the genetic algorithm.