Daniel Lanza
Daniel Lanza
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Cited by
ECJ+ HADOOP: an easy way to deploy massive runs of evolutionary algorithms
F Chávez, F Fernández, C Benavides, D Lanza, J Villegas, L Trujillo, ...
European Conference on the Applications of Evolutionary Computation, 91-106, 2016
Time and individual duration in genetic programming
FF de Vega, G Olague, D Lanza, W Banzhaf, E Goodman, ...
IEEE Access 8, 38692-38713, 2020
Scale out databases for CERN use cases
Z Baranowski, M Grzybek, L Canali, DL Garcia, K Surdy
Journal of physics: Conference series 664 (4), 042002, 2015
Profiting from several recommendation algorithms using a scalable approach
D Lanza, F Chávez, F Fernandez, M Garcia-Valdez, L Trujillo, G Olague
NEO 2015, 357-375, 2017
Deploying massive runs of evolutionary algorithms with ECJ and Hadoop: Reducing interest points required for face recognition
F Chavez, F Fernández de Vega, D Lanza, C Benavides, J Villegas, ...
The International Journal of High Performance Computing Applications 32 (5 …, 2018
It is time for new perspectives on how to fight bloat in GP
FF Vega, G Olague, F Chávez, D Lanza, W Banzhaf, E Goodman
Genetic Programming Theory and Practice XVII, 25-38, 2020
It is Time for New Perspectives on How to Fight Bloat in GP
F Fernández de Vega, G Olague, F Chávez, D Lanza, W Banzhaf, ...
arXiv e-prints, arXiv: 2005.00603, 2020
submitter: Profiting from Several Recommendation Algorithms Using a Scalable Approach
D Lanza, F Fernandez, F Chávez, L Trujillo, M Garcia-Valdez, G Olague
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