@InProceedings{merelo26:ola,
  author =       {JJ Merelo and Cecilia Merelo Molina},
  title =        {Best practices in measuring energy consumption in population-based metaheuristics},
  booktitle = {Proceedings OLA'26 International Conference on Optimization and Learning},
  year =      2026,
  url = {https://vb.svako.lt/object/elaba:291738676/291738676.pdf#page=188},
  pages =     {183--194}}

@misc{lion26,
author={Merelo-Guervós, Juan J. and Merelo-Molina, Cecilia and García-Sánchez, Pablo and García-Valdez, Mario},
title={Is there a (carbon-) free lunch? Energy/performance tradeoffs in population-based metaheuristics},
note={Accepted, LION 20},
year=2026,
month="January"}

@misc{bna25,
year = {2025},
month = {11},
url = {https://hdl.handle.net/10481/107864},
abstract = {Green computing tries to push a series of best practices that, in general, reduce the amount of energy consumed to perform a given piece of work. There are no fixed rules for {\em greening} an algorithm implementation, which means that we need to create a methodology that, after profiling the energy spent by an algorithm implementation, comes up with specific rules that will optimize the amount of energy spent. In population based algorithms, the exploration/exploitation balance is one of the most critical aspects. The algorithm we will be working with in this paper called Brave New Algorithm was designed with the main objective of keeping that balance in an optimal way through the stratification of the population. In this paper we will analyze how this balance affects the energy consumption of the algorithm.},
organization = {University of Granada},
keywords = {Green Computing},
keywords = {Energy profiling},
keywords = {Metaheuristics},
title = {Analyzing how the exploration/exploitation trade off in biologically-inspired algorithms affects energy consumption},
author = {Merelo Guervos, Juan Julián and Merelo-Molina, Cecilia},
}