Research Output per year
Personal profile
Research profile
Anil Yaman is a PhD candidate 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. Anil is currently involved in the H2020-FETOPEN project “PHOENIX: Exploring the Unknown through Reincarnation and Co-evolution”.
Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.
Evolutionary algorithms
Engineering & Materials Science
Evolutionary Algorithms
Mathematics
Evolutionary Computation
Mathematics
Type 2 Diabetes Mellitus
Medicine & Life Sciences
Ontology
Engineering & Materials Science
Population
Medicine & Life Sciences
Knowledge
Mathematics
Parameter Selection
Mathematics
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2014 2019
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
Evolutionary approach to constructing a deep feedforward neural network for prediction of electronic coupling elements in molecular materials
Çaylak, O., Yaman, A. & Baumeier, B., 1 Jan 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
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., 1904.01709v1Research output: Contribution to journal › Article › Academic › peer-review
Open Access
File
Plasticity
Neurons
Chemical activation
Reinforcement
Genetic algorithms
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
Learning with delayed synaptic plasticity
Yaman, A., Iacca, G., Mocanu, D., Fletcher, G. & Pechenizkiy, M., 2019, (Accepted/In press) The Genetic and Evolutionary Computation Conference. arXiv.orgResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Neurons
Plasticity
Chemical activation
Reinforcement
Neural networks