Fitness Landscape-Based Parameter Tuning Method for Evolutionary Algorithms for Computing Unique Input Output Sequences

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Unique Input Output (UIO) sequences are used in conformance testing of Finite state machines (FSMs). Evolutionary algorithms (EAs) have recently been employed to search UIOs. However, the problem of tuning evolutionary algorithm parameters remains unsolved. In this paper, a number of features of fitness landscapes were computed to characterize the UIO instance, and a set of EA parameter settings were labeled with either ’good’ or ’bad’ for each UIO instance, and then a predictor mapping features of a UIO instance to ’good’ EA parameter settings is trained. For a given UIO instance, we use this predictor to find good EA parameter settings, and the experimental results have shown that the correct rate of predicting ’good’ EA parameters was greater than 93%. Although the experimental study in this paper was carried out on the UIO problem, the paper actually addresses a very important issue, i.e., a systematic and principled method of tuning parameters for search algorithms. This is the first time that a systematic and principled framework has been proposed in Search-Based Software Engineering for parameter tuning, by using machine learning techniques to learn good parameter values.