• 19 Citations
20142019
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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”.

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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

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Research Output 2014 2019

  • 19 Citations
  • 8 Conference contribution
  • 5 Article
1 Citation (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 proceedingConference contributionAcademicpeer-review

Open Access
fitness
optimization
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., 1904.01709v1

Research output: Contribution to journalArticleAcademic

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 proceedingConference contributionAcademicpeer-review

Open Access
sampling
optimization
simplification

Learning with delayed synaptic plasticity

Yaman, A., Iacca, G., Mocanu, D., Fletcher, G. & Pechenizkiy, M., 2019, The Genetic and Evolutionary Computation Conference. arXiv.org, 10 p. 1903.09393v2

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Neurons
Plasticity
Chemical activation
Reinforcement
Neural networks