  In the time series classification domain, shapelets are small time series
that are discriminative for a certain class. It has been shown that classifiers
are able to achieve state-of-the-art results on a plethora of datasets by
taking as input distances from the input time series to different
discriminative shapelets. Additionally, these shapelets can easily be
visualized and thus possess an interpretable characteristic, making them very
appealing in critical domains, such as the health care domain, where
longitudinal data is ubiquitous. In this study, a new paradigm for shapelet
discovery is proposed, which is based upon evolutionary computation. The
advantages of the proposed approach are that (i) it is gradient-free, which
could allow to escape from local optima more easily and to find suited
candidates more easily and supports non-differentiable objectives, (ii) no
brute-force search is required, which drastically reduces the computational
complexity by several orders of magnitude, (iii) the total amount of shapelets
and length of each of these shapelets are evolved jointly with the shapelets
themselves, alleviating the need to specify this beforehand, (iv) entire sets
are evaluated at once as opposed to single shapelets, which results in smaller
final sets with less similar shapelets that result in similar predictive
performances, and (v) discovered shapelets do not need to be a subsequence of
the input time series. We present the results of experiments which validate the
enumerated advantages.
