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This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automatabased programming. Metaheuristic algorithms commonly applied to this problem often use FSM mutations (small changes in the FSM structure) for solution construction. Most of them do not employ the specifics of FSMs in their work. We propose a new simple method for improving performance of these algorithms. The basic idea is to mark those transitions of FSMs that were used duringdoi:10.1109/icmla.2013.111 dblp:conf/icmla/ChivilikhinU13 fatcat:isnch4nsfbcqxkj2wf3bwxeop4