1 citations · 2 across the 4 of their papers we have counts for
4 papers
TurboGP: A flexible and advanced python based GP library
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. TurboGP implements modern features not availa…
Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search
Cosijopii Garcia-Garcia, Alicia Morales-Reyes, Hugo Jair Escalante
We propose a novel approach for the challenge of designing less complex yet highly effective convolutional neural networks (CNNs) through the use of cartesian genetic programming (…
Towards Deep Representation Learning with Genetic Programming
Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante
Genetic Programming (GP) is an evolutionary algorithm commonly used for machine learning tasks. In this paper we present a method that allows GP to transform the representation of…
Term-Weighting Learning via Genetic Programming for Text Classification
Hugo Jair Escalante, Mauricio A. García-Limón, Alicia Morales-Reyes +3
This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents wi…