Genetic algorithm for induction of finite automata with continuous and discrete output actions

In this paper, we describe a genetic algorithm for induction of finite automata with continuous and discrete output actions. Input data for the algorithm is a set of tests. Each test consists of two sequences: input events and output actions. In previous works output actions were discrete, i.e. selected from the finite set, in this work output actions can also be continuous, i.e. represented by real numbers. Only the structure of automaton transitions graph is evolved by the genetic algorithm. Values of output actions are found using transition labeling algorithm, which aim is to maximize the value of fitness function. New transition labeling algorithm is proposed. It also works with continuous output actions and is based on equations system solving. In case of proper selection of fitness function, equations in this system are linear and it can be solved by the Gaussian elimination method. The unmanned airplane performing the loop is considered as an example of the controlled object.