Search Results for author: Mauro Castelli

Found 9 papers, 3 papers with code

GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming

no code implementations8 Jun 2021 Leonardo Trujillo, Jose Manuel Muñoz Contreras, Daniel E Hernandez, Mauro Castelli, Juan J Tapia

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation.

Salp Swarm Optimization: a Critical Review

1 code implementation3 Jun 2021 Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni

In the crowded environment of bio-inspired population-based meta-heuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.

Towards an evolutionary-based approach for natural language processing

no code implementations23 Apr 2020 Luca Manzoni, Domagoj Jakobovic, Luca Mariot, Stjepan Picek, Mauro Castelli

Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community.

CoInGP: Convolutional Inpainting with Genetic Programming

1 code implementation23 Apr 2020 Domagoj Jakobovic, Luca Manzoni, Luca Mariot, Stjepan Picek, Mauro Castelli

In the second experiment, we train a GP convolutional predictor on two degraded images, removing around 20% of their pixels.

Pruning Techniques for Mixed Ensembles of Genetic Programming Models

1 code implementation23 Jan 2018 Mauro Castelli, Ivo Gonçalves, Luca Manzoni, Leonardo Vanneschi

The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models.

A Distance Between Populations for n-Points Crossover in Genetic Algorithms

no code implementations3 Jul 2017 Mauro Castelli, Gianpiero Cattaneo, Luca Manzoni, Leonardo Vanneschi

Genetic algorithms (GAs) are an optimization technique that has been successfully used on many real-world problems.

Unsure When to Stop? Ask Your Semantic Neighbors

no code implementations19 Jun 2017 Ivo Gonçalves, Sara Silva, Carlos M. Fonseca, Mauro Castelli

The usage of the proposed semantic stopping criteria in conjunction with the computation of optimal mutation/learning steps also results in small trees and neural networks.

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