Search Results for author: Luca Manzoni

Found 7 papers, 4 papers with code

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.

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.

Tip the Balance: Improving Exploration of Balanced Crossover Operators by Adaptive Bias

no code implementations23 Apr 2020 Luca Manzoni, Luca Mariot, Eva Tuba

Experiments show that improving the exploration of the search space with this adaptive bias strategy is beneficial for the GA performances in terms of the number of optimal solutions found for the balanced nonlinear Boolean functions problem.

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.

Balanced Crossover Operators in Genetic Algorithms

1 code implementation23 Apr 2019 Luca Manzoni, Luca Mariot, Eva Tuba

Furthermore, in two out of three crossovers, the "left-to-right" version performs better than the "shuffled" version.

Combinatorial Optimization

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.

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