@inproceedings{mateos-et-al2-gecco2025,
  author       = {Mateos-Melero, Enrique and Moralejo-Piñas, Javier and Martínez-Gil,
    Francisco and Soriano, María and Fernández, Fernando},
  title        = {Evolutionary Optimization of the Gas/Charging Stations Topology for the
    Electric Vehicle Market},
  year         = 2025,
  isbn         = 9798400714641,
  publisher    = {Association for Computing Machinery},
  address      = {New York, NY, USA},
  url          = {https://doi.org/10.1145/3712255.3726622},
  doi          = {10.1145/3712255.3726622},
  abstract     = {This study applies Evolutionary Algorithms (EAs) to optimize the
    profitability of a hybrid refueling network for conventional and electric vehicles.
    Three strategies are explored: reconfiguring existing stations, siting new ones, and
    combining both methods. A multiagent simulation models stakeholder interactions in a
    Spanish city, incorporating behavioral dynamics between users and operators. The
    approach features problem-specific objective functions, a compact encoding scheme, and
    a heuristic to reduce computational cost. Results show that small, strategically
    located mixed-use stations maximize profitability and support EV adoption.},
  booktitle    = {Proceedings of the Genetic and Evolutionary Computation Conference
    Companion},
  pages        = {867–870},
  numpages     = 4,
  keywords     = {electric vehicle market, charging facility location, genetic algorithms,
    intelligent agents},
  location     = {NH Malaga Hotel, Malaga, Spain},
  series       = {GECCO '25 Companion},
}