Genetic Algorithms in Control Systems Engineering

Genetic algorithms (GAs) are global, parallel, stochastic search methods, founded on Darwinian evolutionary principles. Many variations exist, including genetic programming and multiobjective algorithms. During the last decade GAs have been applied in a variety of areas, with varying degrees of success within each. A significant contribution has been made within control systems engineering. GAs exhibit considerable robustness in problem domains that are not conducive to formal, rigorous, classical analysis. They are not limited by typical control problem attributes such as ill-behaved objective functions, the existence of constraints, and variations in the nature of control variables. GA software tools are available, but there is no ‘industry standard’. The computational complexity of the GA has proved to be the chief impediment to real-time application of the technique. Hence, the majority of applications that use GAs are, by nature, off-line. GAs have been used to optimise both structure and parameter values for both controllers and plant models. They have also been applied to fault diagnosis, stability analysis, robot path-planning, and combinatorial problems (such as scheduling and bin-packing). Hybrid approaches have proved popular, with GAs being integrated in fuzzy logic and neural computing schemes. The GA has been used as the population-based engine for multiobjective optimisers. Multiple, Pareto-optimal, solutions can be represented simultaneously. In such schemes, a decision-maker can lead the direction of future search. Interesting future developments are anticipated in on-line applications and multiobjective search and decision-making.