• 921 Citations
  • 15 h-Index
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Fingerprint Dive into the research topics where Mauro Castelli is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Genetic programming Engineering & Materials Science
Genetic Programming Mathematics
Semantics Engineering & Materials Science
Artificial intelligence Engineering & Materials Science
Learning systems Engineering & Materials Science
Alignment Mathematics
Evolutionary algorithms Engineering & Materials Science
Genetic algorithms Engineering & Materials Science

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Research Output 2012 2019

A distance between populations for n-points crossover in genetic algorithms

Castelli, M., Cattaneo, G., Manzoni, L. & Vanneschi, L., Feb 2019, In : Swarm and Evolutionary Computation. 44, February, p. 636-645 10 p.

Research output: Contribution to journalArticle

Genetic algorithms
Genetic Algorithm
2 Citations (Scopus)

Alignment-based genetic programming for real life applications

Vanneschi, L., Castelli, M., Scott, K. & Trujillo, L., Feb 2019, In : Swarm and Evolutionary Computation. 44, February, p. 840-851 12 p.

Research output: Contribution to journalArticle

Genetic programming
Genetic Programming
Optimal Solution
Direct Search
1 Citation (Scopus)
Neural networks
Network layers
Image classification
Network architecture
Support vector machines

Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S. & Merelli, I., 1 Jan 2019, Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III. Krzhizhanovskaya, V. V., Lees, M. H., Sloot, P. M. A., Dongarra, J. J., Rodrigues, J. M. F., Cardoso, P. J. S., Monteiro, J. & Lam, R. (eds.). Springer Verlag, p. 31-44 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11538 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Messenger RNA
Learning systems
Machine Learning

Evolving multidimensional transformations for symbolic regression with M3GP

Muñoz, L., Trujillo, L., Silva, S., Castelli, M. & Vanneschi, L., 1 Jun 2019, In : Memetic computing. 11, 2, p. 111–126 16 p.

Research output: Contribution to journalArticle

Symbolic Regression
Genetic programming
Parameter estimation
Supervised Classification