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Selected Publications

Cultural Evolution & Social Learning:

  1. Yaman A*, Shen Tian*, Björn Lindström. Semantic knowledge guides innovation and drives cultural evolution. Proceedings of the National Academy of Sciences (PNAS). 123 (22) e2530750123. 2026.
    (*Equal contribution).
     

  2. Yaman A, Leibo JZ, Iacca G, Lee SW. The emergence of division of labour through decentralized social sanctioning. Proceedings of the Royal Society B. 290: 20231716. 2023. https://doi.org/10.1098/rspb.2023.1716
     

  3. Yaman A, Bredeche N, Çaylak O, Leibo JZ, Lee SW. Meta-control of social learning strategies. PLOS Computational Biology 18(2): e1009882. https://doi.org/10.1371/journal.pcbi.1009882.

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Biologically-inspired Learning:

  1. Yaman A, Iacca G, Mocanu DC, Coler M, Fletcher G, Pechenizkiy M. Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. Evolutionary Computation. 2020. DOI: 10.1162/evco_a_00286
    arXiv preprint arXiv:1904.01709.
     

  2. Yaman A, van der Lee T, Iacca G. Online distributed evolutionary optimization of time division multiple access protocols. Expert Systems with Applications, 211, 118627.
     

  3. Yaman A, Iacca G. Distributed Embodied Evolution over Networks. Applied Soft Computing. 101:106993, 2021. DOI: 10.1016/j.asoc.2020.106993 
     

  4. Yaman A, Iacca G, Mocanu DC, Fletcher G, Pechenizkiy M. Novelty Producing Synaptic Plasticity, In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO ’20). ACM, New York, NY, USA, 93–94. DOI: https://doi.org/10.1145/3377929.3389976.
     

  5. Yaman A, Iacca G, Mocanu DC, Fletcher G, Pechenizkiy M. Learning with Delayed Synaptic Plasticity. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), Manuel López-Ibáñez (Ed.). ACM, New York, NY, USA, 152-160, 2019. DOI: https://doi.org/10.1145/3321707.3321723.
     

Evolutionary Algorithms: 

  1. Triebold, C, Yaman, A. Evolving generalist controllers to handle a wide range of morphological variations​
    https://doi.org/10.1145/3638529.3654116. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '24). Association for Computing Machinery, New York, NY, USA, 1137–1145.
    (*Bachelor Thesis 2023, received Amsterdam AI Thesis award*)
     

  2. Hall, O., Yaman, A. Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_13
    (*Best Paper Award*)
     
  3. Çaylak O, Yaman A, Baumeier B. Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials. Journal of Chemical Theory and Computation. 2019.
     
  4. Signorelli, F., & Yaman, A. A Perturbation and Speciation-Based Algorithm for Dynamic Optimization Uninformed of Change. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '25)
     

  5. Custode, L. L., Caraffini, F., Yaman, A., & Iacca, G. An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '24) (pp. 1838-1845). https://dl.acm.org/doi/abs/10.1145/3638530.3664163
     

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