  The Zoetrope Genetic Programming (ZGP) algorithm is based on an original
representation for mathematical expressions, targeting evolutionary symbolic
regression.The zoetropic representation uses repeated fusion operations between
partial expressions, starting from the terminal set. Repeated fusions within an
individual gradually generate more complex expressions, ending up in what can
be viewed as new features. These features are then linearly combined to best
fit the training data. ZGP individuals then undergo specific crossover and
mutation operators, and selection takes place between parents and offspring.
ZGP is validated using a large number of public domain regression datasets, and
compared to other symbolic regression algorithms, as well as to traditional
machine learning algorithms. ZGP reaches state-of-the-art performance with
respect to both types of algorithms, and demonstrates a low computational time
compared to other symbolic regression approaches.
