TurboGP: A flexible and advanced python based GP library

no code implementations • 31 Aug 2023 • Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante

We introduce TurboGP, a Genetic Programming (GP) library fully written in Python and specifically designed for machine learning tasks.

Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search

no code implementations • 5 Jun 2023 • Cosijopii Garcia-Garcia, Alicia Morales-Reyes, Hugo Jair Escalante

Our approach combines real-based and block-chained CNNs representations based on CGP for optimization in the continuous domain using multi-objective evolutionary algorithms (MOEAs).

Evolutionary Algorithms Neural Architecture Search

Towards Deep Representation Learning with Genetic Programming

no code implementations • 20 Feb 2018 • Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante

Genetic Programming (GP) is an evolutionary algorithm commonly used for machine learning tasks.

BIG-bench Machine Learning Representation Learning

Term-Weighting Learning via Genetic Programming for Text Classification

no code implementations • 2 Oct 2014 • Hugo Jair Escalante, Mauricio A. García-Limón, Alicia Morales-Reyes, Mario Graff, Manuel Montes-y-Gómez, Eduardo F. Morales

We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification.

General Classification Image Classification +2