Research Output per year
Personal profile
Research profile
Anil Yaman received his PhD degree in Mathematics and Computer Science in the Eindhoven University of Technology. He holds a B.S. degree from the Karadeniz Technical University (Turkey, 2010), and an M.S. degree from the City College of the City University of New York (USA, 2014). From 2013 to 2015, Anil held a research position at the Department of Biomedical Informatics in Columbia University; and from 2015 to 2016, he joined to INCAS³, an independent research institute founded in Assen, the Nederlands. His main areas of research include topics in computational intelligence, specifically naturally inspired algorithms specializing in evolutionary computation, artificial neural networks, and swarm intelligence. From 2015 to 2019, Anil involved in the H2020-FETOPEN project “PHOENIX: Exploring the Unknown through Reincarnation and Co-evolution”.
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Evolutionary algorithms
Engineering & Materials Science
Plasticity
Engineering & Materials Science
Neurons
Engineering & Materials Science
Evolutionary Algorithms
Mathematics
Neural networks
Engineering & Materials Science
Feedforward neural networks
Engineering & Materials Science
Evolutionary Computation
Mathematics
Chemical activation
Engineering & Materials Science
Network
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Research Output 2014 2019
2
Citations
(Scopus)
A comparison of three differential evolution strategies in terms of early convergence with different population sizes
Yaman, A., Iacca, G. & Caraffini, F., 12 Feb 2019, Proceedings LeGO 2018, 14th International Global Optimization Workshop. Deutz, A. H., Hille, S. C., Sergeyev, Y. D. & Emmerich, M. T. M. (eds.). American Institute of Physics, 3 p. 20002. (AIP Conference Proceedings ; vol. 2070).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open Access
fitness
optimization
1
Citation
(Scopus)
33
Downloads
(Pure)
Evolutionary approach to constructing a deep feedforward neural network for prediction of electronic coupling elements in molecular materials
Çaylak, O., Yaman, A. & Baumeier, B., 12 Mar 2019, In : Journal of Chemical Theory and Computation. 15, 3, p. 1777-1784 8 p.Research output: Contribution to journal › Article › Academic › peer-review
Open Access
File
Feedforward neural networks
predictions
electronics
Carrier mobility
Aluminum
Evolution of biologically inspired learning in artificial neural networks
Yaman, A., 19 Nov 2019, Eindhoven: Technische Universiteit Eindhoven. 149 p.Research output: Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e)
Open Access
File
Evolving plasticity for autonomous learning under changing environmental conditions
Yaman, A., Mocanu, D., Iacca, G., Coler, M., Fletcher, G. & Pechenizkiy, M., 2019, In : arXiv. 26 p., 1904.01709v1.Research output: Contribution to journal › Article › Academic
Open Access
File
Plasticity
Neurons
Chemical activation
Reinforcement
Genetic algorithms
1
Citation
(Scopus)
Improving (1+1) covariance matrix adaptation evolution strategy: a simple yet efficient approach
Caraffini, F., Iacca, G. & Yaman, A., 12 Feb 2019, Proceedings LeGO 2018 : 14th International Global Optimization Workshop. Deutz, A. H., Hille, S. C., Sergeyev, Y. D. & Emmerich, M. T. M. (eds.). Maryland: American Institute of Physics, 4 p. 20004. (AIP Conference Proceedings; vol. 2070).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open Access
sampling
optimization
simplification
Press / Media
A bio-inspired approach to enhance learning in ANNs
4/04/19
1 item of Media coverage
Press/Media: Expert Comment