Optimal spanning tree reconstruction in symbolic regression
This addresses symbolic regression model generation, which is incremental as it builds on existing graph-based methods.
The paper tackles the problem of generating optimal regression models by reconstructing model structures from weighted colored graphs, achieving this through a novel minimum spanning tree reconstruction algorithm based on the prize-collecting Steiner tree approach.
This paper investigates the problem of regression model generation. A model is a superposition of primitive functions. The model structure is described by a weighted colored graph. Each graph vertex corresponds to some primitive function. An edge assigns a superposition of two functions. The weight of an edge equals the probability of superposition. To generate an optimal model one has to reconstruct its structure from its graph adjacency matrix. The proposed algorithm reconstructs the~minimum spanning tree from the~weighted colored graph. This paper presents a novel solution based on the prize-collecting Steiner tree algorithm. This algorithm is compared with its alternatives.