The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules λ to estimate probabilistic labels p(y|λ) for the entire data set. These …
Geometric Semantic Genetic Programming (GSGP) is a method that exploits the geometric properties describing the spatial relationship between possible solutions to a problem in an n-dimensional semantic space. In symbolic regression problems, n is …
The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent matter in the research community. This paper takes a new step in this direction, proposing a different approach to measure the quality of the symbolic …
Advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression. …
Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic …
Automatic Machine Learning is a growing area of machine learning that has a similar objective to the area of hyper-heuristics: to automatically recommend optimized pipelines, algorithms or appropriate parameters to specific tasks without much …
In the last years, different approaches have been proposed to introduce semantic information to genetic programming. In particular, the geometric semantic genetic programming (GSGP) and the interesting properties of its evolutionary operators have …
Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. …
Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n …
Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously …