Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

Entropy (Basel). 2020 Dec 27;23(1):28. doi: 10.3390/e23010028.


In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models' design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.

Keywords: automated learning; co-design; evolutionary learning; generative design; genetic programming.