7 citations · 8 across the 2 of their papers we have counts for
3 papers
cs.NE2024★ 1 cited
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
Javier Poyatos, Javier Del Ser, Salvador Garcia +6
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems de…
cs.NE2024★ 7 cited
Explaining Genetic Programming Trees using Large Language Models
Paula Maddigan, Andrew Lensen, Bing Xue
Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of lev…
cs.NE2024
Genetic Programming for Explainable Manifold Learning
Ben Cravens, Andrew Lensen, Paula Maddigan +1
Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thus enhancing both the efficiency and…