no code implementations • 8 Feb 2024 • Pedro A. Castillo, Maribel García Arenas, Nuria Rico, Antonio Miguel Mora, Pablo García-Sánchez, Juan Luis Jiménez Laredo, Juan Julián Merelo Guervós
When search methods are being designed it is very important to know which parameters have the greatest influence on the behaviour and performance of the algorithm.
no code implementations • 19 Nov 2020 • Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee, Kevin Hammond, Colin G. Johnson, Ahmed Kheiri, Faustyna Krawiec, J. J. Merelo, Leandro L. Minku, Ender Özcan, Gisele L. Pappa, Pablo García-Sánchez, Kenneth Sörensen, Stefan Voß, Markus Wagner, David R. White
We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.
1 code implementation • 29 Dec 2017 • Juan J. Merelo-Guervós, Antonio Fernández-Ares, Antonio Álvarez Caballero, Pablo García-Sánchez, Victor Rivas
The game Starcraft is one of the most interesting arenas to test new machine learning and computational intelligence techniques; however, StarCraft matches take a long time and creating a good dataset for training can be hard.
no code implementations • 7 Jan 2016 • Juan-J. Merelo, Mario García-Valdez, Pedro A. Castillo, Pablo García-Sánchez, P. de las Cuevas, Nuria Rico
We present such an application for running distributed volunteer-based evolutionary algorithm experiments, and we make a series of measurements to establish the speed of JavaScript in evolutionary algorithms that can serve as a baseline for comparison with other distributed computing experiments.
1 code implementation • 3 Nov 2015 • Juan-J. Merelo, Pablo García-Sánchez, Mario García-Valdez, Israel Blancas
It is quite usual when an evolutionary algorithm tool or library uses a language other than C, C++, Java or Matlab that a reviewer or the audience questions its usefulness based on the speed of those other languages, purportedly slower than the aforementioned ones.
no code implementations • 22 Mar 2015 • Juan Julián Merelo-Guervós, Pablo García-Sánchez
From the era of big science we are back to the "do it yourself", where you do not have any money to buy clusters or subscribe to grids but still have algorithms that crave many computing nodes and need them to measure scalability.