Find also an updated list of publications on my institutional archive at

  • Joanna Turner, Qinggang Meng, Gerald Schaefer, and Andrea Soltoggio (2018) “Fast Consensus for Fully Distributed Multi-Agent Task Allocation”. In Proceedings of ACM SAC Conference, Pau, France, April 9-13, 2018 (SAC’18),
  • Bahroun, Y, Hunsicker, E, and Soltoggio, A. (2017) “Neural Networks for Efficient Nonlinear Online Clustering”, ICONIP 2017, International Conference on Neural Information Processing.  Download preprint pdf
  • Soltoggio, A, Stanley, K. O., Risi, S. (2017) Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Neural Networks. arXiv:
  • Turner, J., Meng, Q., Schaefer, G., Whitbrook, A. and Soltoggio, A. (2017) Distributed Task Rescheduling With Time Constraints for the Optimisation of Total Task Allocations in a Multi-Robot System. IEEE Transactions on Cybernetics (to appear, DOI: 10.1109/TCYB.2017.2743164)
  • Bahroun, Y, Hunsicker, E, and Soltoggio, A. (2017) “Building Efficient Deep Hebbian Networks for Image Classification Tasks”, ICANN 2017, International Conference on Artificial Neural Networks (to appear) Download preprint pdf
  • Andrea Soltoggio (2014) Short-term plasticity as cause-effect hypothesis testing in distal reward learning, Biological Cybernetics, Feb 2015, Vol 109, p75-94, DOI: 10.1007/s00422-014-0628-0 Online preprint on Arxiv since Feb 2014.
  • Alessandro Fontana, Andrea Soltoggio, and Borys Wrobel (2014) POET: an evo-devo method to optimize the weights of a large artificial neural networks. In: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV). Cambridge, MA: MIT Press, 2014 (8 pages).
  • Andrea Soltoggio, Felix Reinhart, Andre Lemme and Jochen Steil (2013) Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards. The Third Joint IEEE International Conference of Development and Learning and on Epigenetic Robotics, Osaka, Japan, August 18-22, 2013, Download preprint pdf Download software
  • Andrea Soltoggio, Andre Lemme (2013) Movement Primitives as a Robotic Tool to Interpret Trajectories in Learning-by-doing. International Journal of Automation and Computing, Vol 10, No. 5, Pages 375-385.
    Watch support video on YouTube | Download preprint pdf
  • Ben Jones, Andrea Soltoggio, Xin Yao, Bernhard Sendhoff (2011) Evolution of Neural Symmetry and its Coupled Alignment to Body Plan Morphology, Genetic and Evolutionary Computation Conference 2011, 12th-16th July, Dublin, Irland. Nominated for best paper award.
    Download pdf.
  • Andrea Soltoggio and Ben Jones (2009) Novelty of Behaviour as a Basis for the Neuro-evolution of Operant Reward Learning, Genetic and Evolutionary Computation Conference 2009, 8th-12th July, MontrÈal, Canada.
    Download pdf.
  • Andrea Soltoggio (2008) Evolutionary and Computational Advantages of Neuromodulated Plasticity, Ph.D. thesis. University of Birmingham, UK. October 2008.
    Dowload pdf.
  • Andrea Soltoggio (2008) Neural Plasticity and Minimal Topologies for Reward-based Learning, In the proceedings of the 8th International Conference on Hybrid Intelligent Systems,10th-12th September 2008. Barcellona, Spain.
    Download pdf.
  • Soltoggio, A., Bullinaria, A. J., Mattiussi, C., Dürr, P. and Floreano, D. (2008) Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems
    Dowload pdf.
  • Dürr, P., Mattiussi, C., Soltoggio, A. and Floreano, D, (2008) Evolvability of Neuromodulated Learning for Robots. Symposium on Bio-inspired Learning and Intelligent Systems for Security, (BLISS) August 2008, Edinburgh, UK.
  • Andrea Soltoggio (2008) Neuromodulation Increases Decision Speed in Dynamic Environments, Proceedings of the 8th International Conference on Epigenetic Robotics, July 2008, Southampton, UK.
    Download pdf.
  • Andrea Soltoggio (2008) Phylogenetic Onset and Dynamics of Neuromodulation in Learning Neural Models, Experiments Meet Theory: Integrated Approaches to Neuroscience, Young Physiologists’ Symposium, July 2008, Cambridge, UK.
    Download pdf.
  • Soltoggio, A., Dürr, P., Mattiussi, C., and Floreano, D. (2007) Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems. Proceedings of the 2007 IEEE Congress on Evolutionary Computation. 25-28 September 2007, Singapore.
    Download pdf.
  • Andrea Soltoggio. Does Learning Elicit Neuromodulation? Evolutionary Search in Reinforcement Learning-like Environments, Dynamics of Learning Behavior and Neuromodulation Workshop, European Congress on Artificial Life (ECAL 2007) 10-14 September 2007, Lisbon, Portugal.
    Poster pdf, Extended abstract pdf.
  • Andrea Soltoggio. A Simple Line Search Operator for Ridged Landscapes. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), July 2006, Seattle, WA, USA.
    Download pdf.
  • Andrea Soltoggio. An Enhanced GA to Improve the Search Process Reliability in Tuning of Control Systems, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), June 2005, Washington, DC, USA.
    Download pdf.
  • Andrea Soltoggio. GP and GA in the Design of a Constrained Control System with Disturbance Rejection, Proceedings of the International Symposium on Intelligent Control, (ISIC 2004), 1-4 September 2004, Taipei, Taiwan.
    Download pdf.
  • Andrea Soltoggio. A Comparison of Genetic Programming and Genetic Algorithms in the Design of a Robust, Saturated Control System, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004), June 2004, Seattle, WA, USA.
    Download pdf.
  • Andrea Soltoggio. A Case Study of GP and GAs in the Design of a Control System, GECCO 2004 Graduate Student Workshop Proceedings, June 2004, Seattle, USA.
  • Andrea Soltoggio. MSc Thesis. NTNU, June 2004, Evolutionary Algorithms in the Design and Tuning of a Control System.
    Download pdf.
  • Andrea Soltoggio. Fordypningsprosjekt. Department of Computer and Information Science, NTNU.
    A Case Study of a Genetically Evolved Control System, November 2003Download pdf.