A fundamental problem in science is the formulation of a model of a system when no a priori knowledge of the process is available. To this end, symbolic regression methods were developed to search for a model’s structure, as well as its parameters, using a stochastic optimization technique known as genetic programming. However, symbolic regression exhibits a well-known phenomenon known as bloat, in which it produces overly complex solutions. We developed a method known as epigenetic local search  that specifically optimizes model structures for conciseness during optimization via a novel developmental encoding. We found that this method produced concise solutions that generalized better to test data. We applied it to the modeling of wind turbine dynamics , as well as fluid dynamics and other non-linear dynamic systems  and showed it could find true underlying physical principles. In comparison to neural network and auto-regressive models, our method produced much simpler and often more accurate models.
La Cava, W., Helmuth, T., Spector, L., & Danai, K. (2015). Genetic Programming with Epigenetic Local Search. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 1055–1062). ACM Press. link, preprint
La Cava, W., Danai, K., & Spector, L. (2016). Inference of compact nonlinear dynamic models by epigenetic local search. Engineering Applications of Artificial Intelligence, 55, 292–306. link, preprint
La Cava, W., Danai, K., Spector, L., Fleming, P., Wright, A., & Lackner, M. (2016). Automatic identification of wind turbine models using evolutionary multiobjective optimization. Renewable Energy, 87, Part 2, 892–902. link, preprint