An Incremental-Evolutionary Approach for Learning Deterministic Finite Automata

This work proposes an approach for learning deterministic finite automata (DFA) that combines incremental learning and evolutionary algorithms. First, the training set is sorted according to the sequence length (from the shortest sequence to the longest one). Then, the training set is divided into a suitable number of groups (M). Next, a DFA population is evolved by using a block of the training set (initially the first group). This process is repeated M times by taken the previously evolved DFA population as initial population and by adding the next sequences group to the previously used block. Finally, an evolutionary algorithm tunes the previously evolved DFA population by using the full training set and the remaining running time. Experiments show that our approach performs well regardless the level of noise present in the training set.