GADF — Genetic Algorithms for distribution fitting

Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to simultaneously identify the distribution type and tune its parameters by exploiting a dataset sampled from the observed random variable and a set of distribution families. The strength of this approach lies in the easiness of the expansion of this set: in fact distributions are simply described by means of their probability density functions and cumulative distribution functions, which are well-known. This approach employs two different score metrics, the Mean Absolute Error and the Kolmogorov-Smirnov test, that are linearly combined in order to find the best fitting distribution. The results obtained in an industrial application are presented and discussed.