Publications

Publications

Publications

Journals:

  1. S. Nesmachnow, G. Colacurcio, D. Rossit, J. Toutouh, and F. Luna (2021) Optimizing household energy planning in Smart cities: a multiobjective approach. Revista Facultad de Ingeniería. Universidad de Antioquia (In press). doi: 10.17533/udea.redin.20200587
  2. I. Lebrusán and J. Toutouh (2021) Car restriction policies for better urban health: a low emission zone in Madrid, Spain. Air Quality, Atmosphere and Health, 14, 333–342. doi: 10.1007/s11869-020-00938-z
  3. A. Camero, J. Toutouh, and E. Alba (2020). Random error sampling-based recurrent neural network architecture optimization. Engineering Applications of Artificial Intelligence, Volume 96, November 2020.doi: 10.1016/j.engappai.2020.103946
  4. I Lebrusán and J. Toutouh (2020) Using smart city tools to evaluate the effectiveness of a low emissions zone in Spain: Madrid Central. Smart Cities. Special Issue: Mobility and IoT for the Smart Cities, 3(2), 456-478. doi: 10.3390/smartcities3020025
  5. U. O’Reilly, J. Toutouh, M. Pertierra, D. Prado-Sanchez, D. Garcia, A. Erb-Luogo, J. Kelly, and E Hemberg (2019). Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters. Genetic Programming and Evolvable Machines, 21, 219–250. doi: 10.1007/s10710-020-09389-y
  6. D. G. Rossit, J. Toutouh, S. Nesmachnow. Exact and heuristic approach for multi-objective garbage accumulation points location in real scenarios. Waste Management Vol. 105,pp. 467-481, 2020. doi: 10.1016/j.wasman.2020.02.016.
  7. A. Camero, J. Toutouh, J. Ferrer, and E. Alba (2019). Waste generation prediction under uncertainty in smart cities through deep neuroevolution. Revista de Ingeniería, No.93, pp. 128-138, 2019. doi: 10.17533/udea.redin.20190736.
  8. J. Toutouh, D. G. Rossit, and S. Nesmachnow (2019). Soft computing methods for multiobjective location of garbage accumulation points in smart cities. Annals of Mathematics and Artificial Intelligence pp. 1-27. 2019. doi: 10.1007/s10472-019-09647-5.
  9. D. G. Rossit, S. Nesmachnow, and J. Toutouh (2019). A bi-objective integer programming model for locating garbage accumulation points: a case study. Revista de Ingeniería, No.93, pp. 70-81, 2019. doi: 10.17533/udea.redin.20190509.
  10. J. Toutouh, J. Arellano, and E. Alba (2018). BiPred: A Bilevel Evolutionary Algorithm for Prediction in Smart Mobility. Sensors, 18(12), 4123. doi: 10.3390/s18124123

Conferences:

  1. I. Lebrusán and J. Toutouh (2021) Smart City Tools to Evaluate Age-Healthy Environments. In: Nesmachnow S., Hernández Callejo L. (eds) Smart Cities. ICSC-CITIES 2020. Communications in Computer and Information Science, vol 1359. Springer, Cham.
  2. J. Toutouh (2021) Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution. In: Nesmachnow S., Hernández Callejo L. (eds) Smart Cities. ICSC-CITIES 2020. Communications in Computer and Information Science, vol 1359. Springer, Cham.
  3. J. Toutouh, E. Hemberg, and U. O’Reilly (2020) Analyzing the Components of Distributed Coevolutionary GAN Training. In International Conference on Parallel Problem Solving from Nature, pp. 552-566, Springer, Cham.
  4. E. Pérez, S. Nesmachnow, J. Toutouh, E. Hemberg, and & U. O’Reily (2020). Parallel/distributed implementation of cellular training for generative adversarial neural networks. In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 512-518. IEEE.
  5. J. Toutouh, E. Hemberg, and U. O’Reilly (2020) Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation In Genetic and Evolutionary Computation Conference (GECCO ’20), pages. 10, 2020. DOI: 10.1145/3377930.3390229
  6. E. Perez, S. Nesmachnow, J. Toutouh, E. Hemberg, and U. O’Reilly (2020). Parallel/distributed implementation of cellular training for generative adversarial neural networks. In 10th IEEE Workshop Parallel/Distributed Combinatorics and Optimization (PDCO 2020), pages 7, 2020.
  7. J. Toutouh, I. Lebrusan, and S. Nesmachnow (2020). Computational intelligence for evaluating the air quality in the center of Madrid, Spain. In International Conference in Optimization and Learning (OLA2020), pp. 115-127., 2020. https://doi.org/10.1007/978-3-030-41913-4_10
  8. G. Colacurcio, S. Nesmachnow, J. Toutouh, F. Luna, and D. Rossit (2019). Multiobjective household energy planning using evolutionary algorithms. In II Ibero-American Congress of Smart Cities (ICSC-CITIES 2019), pages 15, 2019. https://doi.org/10.1007/978-3-030-38889-8_21
  9. I. Lebrusan, J. Toutouh (2019). Assessing the environmental impact of car restrictions policies: Madrid Central case. In II Ibero-American Congress of Smart Cities (ICSC-CITIES 2019), pages 15, 2019. https://doi.org/10.1007/978-3-030-38889-8_2
  10. J. Toutouh, E. Hemberg, and U. O’Reilly (2019) Spatial Evolutionary Generative Adversarial Networks. In Genetic and Evolutionary Computation Conference (GECCO ’19), July 13–17, 2019, Prague, Czech Republic. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3321707.3321860

Book chapters:

  1. J. Toutouh, E. Hemberg, U. O’Reilly. Data Dieting in GAN Training. H. Iba, N. Noman (Eds.), Deep Neural Evolution - Deep Learning with Evolutionary Computation, pages 19, Springer, 2020, Springer (In Press).

Posters:

  1. A System that Scales Robust Generative Adversarial Network Training presented in the MIT College of Computing poster session 2019.
  2. Mustangs: Robust Training of Generative Adversarial Networks by Fostering Diversity presented in the MIT-IBM Watson AI Lab networking and poster reception 2019.
  3. Coevolutionary GANs Training to Foster Diversity presented in the GANocracy: Democratizing GANs 2019.

Invited Talks:

  1. Webinar: Deep Neuroevolution applied to Generative Adversarial Networks organized by Spain AI Association, April 2020.
  2. Webinar: Navigating to Generative Adversarial Networks, a friendly introductio organized by Spain AI Association, April 2020.
  3. Webinar: Lipizzaner: Distributed Coevolution for Resilient Generative Adversarial Networks Training at Universidad de la Republica, Uruguay, April 2020.
  4. Webinar: Applying Generative Adversarial Networks to address Real World Problems: Smart Energy Forecasting at Universidad de la Republica, Uruguay, April 2020.
  5. Lipizzaner: Spatially distributed coevolution for robust and resilient GAN training at Schlumberger (an industrial company that wants to use our open source framework), December 2019.
  6. Spatial Coevolutionary Deep Neural Networks Training. Jamal Toutouh. Universidad de la Republica, Montevideo Uruguay, May 2019.
  7. An Artificial Coevolutionary Framework for Adversarial AI. Jamal Toutouh. Universidad de la Republica, Montevideo Uruguay, May 2019.