Abstract
We propose an evolutionary flow for finite state machine inference through the cooperation of grammatical evolution and a genetic algorithm. This coevolution has two main advantages. First, a high-level description of the target problem is accepted by the flow, being easier and affordable for system designers. Second, the designer does not need to define a training set of input values because it is automatically generated by the genetic algorithm at run time. Our experiments on the sequence recognizer and the vending machine problems obtained the FSM solution in 99.96% and 100% of the optimization runs, respectively.
J. Manuel Colmenar
Associate Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.