30 citations · 49 across the 7 of their papers we have counts for
6 papers · 1 filter
NEVO-GSPT: Population-Based Neural Network Evolution Using Inflate and Deflate Operators
Davide Farinati, Frederico J. J. B. Santos, Leonardo Vanneschi +1
Evolving neural network architectures is a computationally demanding process. Traditional methods often require an extensive search through large architectural spaces and offer lim…
A Study of Fitness Landscapes for Neuroevolution
Nuno M. Rodrigues, Sara Silva, Leonardo Vanneschi
Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of…
Pruning Techniques for Mixed Ensembles of Genetic Programming Models
Mauro Castelli, Ivo Gonçalves, Luca Manzoni +1
The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due…
A Distance Between Populations for n-Points Crossover in Genetic Algorithms
Mauro Castelli, Gianpiero Cattaneo, Luca Manzoni +1
Genetic algorithms (GAs) are an optimization technique that has been successfully used on many real-world problems. There exist different approaches to their theoretical study. In…
An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction
Mauro Castelli, Luca Manzoni, Leonardo Vanneschi
Very recently new genetic operators, called geometric semantic operators, have been defined for genetic programming. Contrarily to standard genetic operators, which are uniquely ba…
Neutral Fitness Landscape in the Cellular Automata Majority Problem
Sébastien Verel, Philippe Collard, Marco Tomassini +1
We study in detail the fitness landscape of a difficult cellular automata computational task: the majority problem. Our results show why this problem landscape is so hard to search…