  The paper presents a method for failure free genetic algorithm optimization
of a system controller. Genetic algorithms present a powerful tool that
facilitates producing near-optimal system controllers. Applied to such methods
of computational intelligence as neural networks or fuzzy logic, these methods
are capable of combining the non-linear mapping capabilities of the latter with
learning the system behavior directly, that is, without a prior model. At the
same time, genetic algorithms routinely produce solutions that lead to the
failure of the controlled system. Such solutions are generally unacceptable for
applications where safe operation must be guaranteed. We present here a method
of design, which allows failure-free application of genetic algorithms through
utilization of SAFE and LEARNING controllers in tandem, where the SAFE
controller recovers the system from dangerous states while the LEARNING
controller learns its behavior. The method has been validated by applying it to
an inherently unstable system of inverted pendulum.
