Automatic evolutionary learning of composite models with knowledge enrichment

This paper provides the main concepts of the knowledge-enriched AutoML approach and shortly describes the current results of the proof of concept implementation within the FEDOT framework. By knowledge enrichment, we mean the insertion of domain-specific models and expert-like meta-heuristics. Also, we involve multi-scale learning as a part of complex models identification. The proposed concepts make it possible to create effective and interpretable composite models.