Journal paper:
Milano, N. and Nolfi, S., 2020. Enhancing Cartesian genetic programming through preferential selection of larger solutions. Evolutionary Intelligence, pp.1-8.
Milano, N., Pagliuca, P. and Nolfi, S., 2019. Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. Evolutionary Intelligence, 12(1), pp.83-95.
Milano, N., Carvalho, J.T. and Nolfi, S., 2019. Moderate environmental variation across generations promotes the evolution of robust solutions. Artificial life, 24(4), pp.277-295.
Pagliuca, P., Milano, N. and Nolfi, S., 2018. Maximizing adaptive power in neuroevolution. PloS one, 13(7).
Milano, N. and Nolfi, S., 2016. Robustness to faults promotes evolvability: Insights from evolving digital circuits. PloS one, 11(7).
Conferences:
Milano, N., Carvalho, J.T. and Nolfi, S., 2017. Environmental variations promotes adaptation in artificial evolution. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
Carvalho, J.T., Milano, N. and Nolfi, S., 2018, July. Evolving Robust Solutions for Stochastically Varying Problems. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.
