©2019 by Anil Yaman.

Featured Blog Posts

©2019 by Anil Yaman.

PUBLICATIONS

  1. Yaman, A. Evolution of Biologically Inspired Learning in Artificial Neural Networks, PhD Thesis, Eindhoven, Technische Universiteit Eindhoven, 2019.

  2. Yaman A, Mocanu DC, Iacca G, Coler M, Fletcher G, Pechenizkiy M. Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions. arXiv preprint arXiv:1904.01709. 2019. 

  3. 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.

  4. 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. 

  5. Caraffini F, Iacca G, Yaman A. Improving (1+ 1) Covariance Matrix Adaptation Evolution Strategy: A Simple Yet Efficient Approach. In AIP Conference Proceedings 2019 Feb 12 (Vol. 2070, No. 1, p. 020004). AIP Publishing. 

  6. Yaman A, Iacca G, Caraffini F. A Comparison of Three Differential Evolution Strategies in Terms of Early Convergence with Different Population Sizes. InAIP Conference Proceedings 2019 Feb 12 (Vol. 2070, No. 1, p. 020002). AIP Publishing. 

  7. Yaman A, Mocanu DC, Iacca G, Fletcher G, Pechenizkiy M. Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution. In Proceedings of the Genetic and Evolutionary Computation Conference 2018 (pp. 569-576). ACM. 

  8. Yaman A, Iacca G, Coler M, Fletcher G, Pechenizkiy M. Multi-strategy Differential Evolution. In: Sim K., Kaufmann P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science, vol 10784. Springer, Cham. 

  9. Sen A, Goldstein A, Chakrabarti S, Shang N, Kang T, Yaman A, Ryan PB, Weng C. The Representativeness of Eligible Patients in Type 2 Diabetes Trials: A Case Study Using GIST 2.0. Journal of the American Medical Informatics Association. 2017 Sep 13;25(3):239-47. 

  10. Yaman A, Hallawa A, Coler M, Iacca G. Presenting the ECO: Evolutionary Computation Ontology. In: Squillero G., Sim K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science, vol 10199. Springer, Cham. 

  11. Hallawa A, Yaman A, Iacca G, Ascheid G. A Framework for Knowledge Integrated Evolutionary Algorithms.  In: Squillero G., Sim K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science, vol 10199. Springer, Cham.

  12. Yaman A, Chakrabarti S, Sen A, Weng C. How Have Cancer Clinical Trial Eligibility Criteria Evolved over Time?. AMIA Summits on Translational Science Proceedings. 2016. (Distinguished Clinical Research Informatics Paper Award).

  13. Chandar P*, Yaman A*, Hoxha J, He Z, Weng C. Similarity-based Recommendation of New Concepts to a Terminology. In AMIA Annual Symposium Proceedings 2015 (Vol. 2015, p. 386). American Medical Informatics Association. (* equal contribution)

  14. Weng C, Yaman A, Lin K, He Z. Trend and Network Analysis of Common Eligibility Features for Cancer Trials in ClinicalTrials. gov. In International Conference on Smart Health 2014 Jul 10 (pp. 130-141). Springer, Cham. 

  15. Yaman A, Lucci S, Gertner I. Evolutionary Algorithm Based Approach for Modeling Autonomously Trading Agents. Intelligent Information Management. 2014 Mar 17;6(02):45.