Control Through Genetic Algorithms

Many real world applications require automatic control. This chapter addresses genetic algorithms to achieve the control, based on their numerous advantages for the difficult problems. First of all a unitary approach of the control through the perspective of the systems theory is presented. There are described examples of control in biology, economy and technical areas in order to highlight the general system behaviors: preventive control, reactive control or combined control. In the next section, fundamentals of genetic algorithms theory are featured: genetic representation, genetic operators, how it works and why it works. Further, two process control systems based on genetic algorithms are described: a chemical process control involving mass transfer, where the genetic algorithms are used in the system identification for a NARMAX model, an important issue with respect to model based control and a job shop scheduling process in manufacturing area where the genetic algorithm is the tool to model the optimization process control.

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