arXiv:2606. 13799v1 Announce Type: cross Abstract: Finding the shortest program that generates a sequence is uncomputable, and for six decades that fact has been mistaken for a wall around finding any generating program.
By Jorge Miguel Silva
arXiv:2606. 12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems.
By Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
arXiv:2605. 29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers.
By Elliot Gestrin, Jendrik Seipp
arXiv:2606. 28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that exploit correlation rather than prediction.
By Artem Andrianov (Cyntegrity Germany GmbH, Hofheim am Taunus, Germany)
arXiv:2606. 31990v1 Announce Type: cross Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions.
By Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira, Stephan Winkler
arXiv:2606. 10237v1 Announce Type: new Abstract: Genetic programming (GP) is based on two important insights.
By Leonardo Trujillo