Multi-Objective Evolutionary Design of Composite Data-Driven Models

The internal structure of the model depends on the type of the There is a variety of approaches that can be used to learning algorithm, so complex data-driven models can consist identify the optimal design of the data-driven model. For of several semi-independent blocks - this approach is usually instance, AutoML solutions can be based on random search referred to as ensembling [2]. There are several techniques to [5], Bayesian optimisation [6], reinforcement learning (RL) build complex models: for example, blending allows creating [7], Monte Carlo tree search [8], sequential model-based single-level ensembles of machine learning (ML) models, and optimization [9], gradient-based approaches [10]. However, stacking allows creating multi-level ones. Other approaches are most of them are less flexible than evolutionary approaches to based on the representation of a model structure (or even the the model design (implemented e.g. in [11]). Their conceptual whole modeling pipeline) as a directed acyclic graph (DAG).