Search Results for author: Mauro Castelli

Found 12 papers, 3 papers with code

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.

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.

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.

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.

Sentence

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.

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 metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.

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.

BIG-bench Machine Learning

The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

no code implementations5 May 2022 Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli

Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems.

Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration

no code implementations7 Jun 2022 Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli

The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves satisfactory results in both the search for the global optimum and the production of a visual approximation of the original benchmark function (in the 2-dimensional case).

Local Search, Semantics, and Genetic Programming: a Global Analysis

no code implementations26 May 2023 Fabio Anselmi, Mauro Castelli, Alberto D'Onofrio, Luca Manzoni, Luca Mariot, Martina Saletta

In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions.

regression

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