11 documents found, page 1 of 2
Geometric Semantic Genetic Programming with Normalized and Standardized Random ...
Bakurov, Illya; Muñoz Contreras, José Manuel; Castelli, Mauro; Rodrigues, Nuno Miguel Duarte; Silva, Sara; Trujillo, Leonardo; Vanneschi, LeonardoBakurov, I., Muñoz Contreras, J. M., Castelli, M., Rodrigues, N., Silva, S., Trujillo, L., & Vanneschi, L. (2024). Geometric Semantic Genetic Programming with Normalized and Standardized Random Programs. Genetic Programming And Evolvable Machines, 25, 1-29. Article 6. https://doi.org/10.1007/s10710-024-09479-1 --- This work was partially supported by FCT, Portugal, through funding of research units MagIC/NOVA I...
Introduction to the peer commentary special section on “Jaws 30” by W. B. Langdon
Vanneschi, Leonardo; Trujillo, LeonardoVanneschi, L., & Trujillo, L. (2023). Introduction to the peer commentary special section on “Jaws 30” by W. B. Langdon. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-2. [18]. https://doi.org/10.1007/s10710-023-09466-y; In 1992, John R. Koza published his first book on Genetic Programming (GP): “Genetic Programming: On the Programming of Comp...
GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
Trujillo, Leonardo; Muñoz Contreras, Jose Manuel; Hernandez, Daniel E.; Castelli, Mauro; Tapia, Juan J.Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming. SoftwareX, 18, 1-7. [101085]. https://doi.org/10.1016/j.softx.2022.101085 -------------------------------Funding Information: We thank Perla Juárez-Smith for her help implementing some of the source code used in this software. We also thank the Tec...
Using Neuroevolution to Design Neural Networks [special issue]
Castelli, Mauro; Medvet, Eric; Trujillo, Leonardo; Manzoni, LucaCastelli, M. (Guest ed.), Medvet, E. (Guest ed.), Trujillo, L., & Manzoni, L. (Guest ed.) (2020). Using Neuroevolution to Design Neural Networks. Computational Intelligence And Neuroscience, 2020.; Deep learning (DL) has gained popularity in the field of machine learning, with DL-based models nowadays used to address complex problems across many different domains. In DL, the training process of a neural network...
Is k Nearest Neighbours Regression Better Than GP?
Vanneschi, Leonardo; Castelli, Mauro; Manzoni, Luca; Silva, Sara; Trujillo, LeonardoVanneschi, L., Castelli, M., Manzoni, L., Silva, S., & Trujillo, L. (2020). Is k Nearest Neighbours Regression Better Than GP? In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 244-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes ...
Unlabeled multi-target regression with genetic programming
Lopez, Uriel; Trujillo, Leonardo; Silva, Sara; Vanneschi, Leonardo; Legrand, PierrickLopez, U., Trujillo, L., Silva, S., Vanneschi, L., & Legrand, P. (2020). Unlabeled multi-target regression with genetic programming. In GECCO 2020: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 976-984). (GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3377930.3389846; Machine Learn...
Evolving multidimensional transformations for symbolic regression with M3GP
Muñoz, Luis; Trujillo, Leonardo; Silva, Sara; Castelli, Mauro; Vanneschi, LeonardoMuñoz, L., Trujillo, L., Silva, S., Castelli, M., & Vanneschi, L. (2019). Evolving multidimensional transformations for symbolic regression with M3GP. Memetic computing, 11(2), 111–126. https://doi.org/10.1007/s12293-018-0274-5; Multidimensional Multiclass Genetic Programming with Multidimensional Populations (M3GP) was originally proposed as a wrapper approach for supervised classification. M3GP searches for t...
Alignment-based genetic programming for real life applications
Vanneschi, Leonardo; Castelli, Mauro; Scott, Kristen; Trujillo, LeonardoVanneschi, L., Castelli, M., Scott, K., & Trujillo, L. (2019). Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation, 44(February), 840-851. DOI: 10.1016/j.swevo.2018.09.006; A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called ...
Local Search is Underused in Genetic Programming
Trujillo, Leonardo; Z-Flores, Emigdio; Juárez-Smith, Perla S.; Legrand, Pierrick; Silva, Sara; Castelli, Mauro; Vanneschi, Leonardo; Schütze, OliverTrujillo, L., Z-Flores, E., Juárez-Smith, P. S., Legrand, P., Silva, S., Castelli, M., ... Muñoz, L. (2018). Local Search is Underused in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Eds.), Genetic Programming Theory and Practice XIV (pp. 119-137). [8] (Genetic and Evolutionary Computation). Springer. https://doi.org/10.1007/978-3-319-97088-2_8; There are two important limitations of st...
A scalable genetic programming approach to integrate miRNA-target predictions
Beretta, Stefano; Castelli, Mauro; Muñoz, Luis; Trujillo, Leonardo; Martínez, Yuliana; Popovič, Aleš; Milanesi, Luciano; Merelli, IvanBeretta, S., Castelli, M., Munoz, L., Trujillo, L., Martinez, Y., Popovic, A., ... Merelli, I. (2018). A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP. Complexity, [4963139]. DOI: 10.1155/2018/4963139; There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are...