• Michael N. Vrahatis

    www.math.upatras.gr/~vrahatis Résumé last updated on February 15, 2020 . Biographical sketch: Michael N. Vrahatis (Greek: Μιχαήλ Ν. Βραχάτης) was born in Kalamata, Greece, on April 27, 1955. He received his Diploma in Mathematics (first academic degree, Ptychio) from the University of Patras in 1978 and his PhD in Mathematics from the same Institution in 1982.His PhD advisor was Prof. Kosmas I. Iordanidis.
  • Two Kinds of Classifications Based on Improved ...

    www.hindawi.com/journals/amp/2017/2131862 K. Parsopoulos and M. Vrahatis, “Unified particle swarm optimization for tackling operations research problems,” in Proceedings of the 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., pp. 53–59, Pasadena, CA, USA. View at: Publisher Site | Google Scholar
  • Optimizing the Probabilistic Neural Network Training ...

    publications.waset.org/.../optimizing-the-probabilistic-neural-network-traini... [19] V. Georgiou, P. Alevizos, and M. Vrahatis, « Fuzzy Evolutionary Probabilistic Neural Networks », Artificial Neural Networks in Pattern Recognition, p. 113–124, 2008. [20] P. Burrascano, « Learning vector quantization for the probabilistic neural network », IEEE Transactions on Neural Networks / a Publication of the IEEE Neural ...
  • Giotto:. a Code for the Nonlinear Analysis of Area ...

    ui.adsabs.harvard.edu/abs/1995IJMPC...6..651S/abstract Giovannozzi, M.; Vrahatis, M. N. Abstract. An interactive code for the analysis of nonlinear area-preserving mappings is described; several facilities allow the user to draw phase portraits, make zooming, and use colors. The perturbative approach of normal forms and all the standard tools for the analysis of the nonlinear dynamics (Fourier ...
  • Dynamic search trajectory methods for global optimization ...

    link.springer.com/article/10.1007/s10472-019-09661-7 A detailed review of the dynamic search trajectory methods for global optimization is given. In addition, a family of dynamic search trajectories methods that are created using numerical methods for solving autonomous ordinary differential equations is presented. Furthermore, a strategy for developing globally convergent methods that is applicable to the proposed family of methods is given and ...
  • Elliptic Quantum Billiard - ScienceDirect

    www.sciencedirect.com/science/article/pii/S0003491697957158 The exact and semiclassical quantum mechanics of the elliptic billiard is investigated. The classical system is integrable and exhibits a separatrix, dividing the phase space into regions of oscillatory and rotational motion.
  • CiteSeerX — Oriented k-windows: A PCA driven clustering method

    citeseerx.ist.psu.edu/viewdoc/summary CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): on subsets of the dataset to better approximate clusters. We focus on a specific density-based clustering algorithm, k-Windows, that holds particular promise for problems of moderate dimensionality. We show that the resulting algorithm, we call Oriented k-Windows (OkW), is able to steer the clustering procedure by ...
  • Opposition chaotic fitness mutation based adaptive inertia ...

    dl.acm.org/doi/10.1016/j.asoc.2016.01.019 Graphical abstractDisplay Omitted HighlightsA feature selection method based on binary particle swarm optimization is presented.Fitness based adaptive inertia weight is integrated with the binary particle swarm optimization to dynamically control the exploration and exploitation of the particle in the search space.Opposition and mutation are integrated with the binary particle swarm ...
  • No Free Lunch Theorem: A Review | SpringerLink

    link.springer.com/chapter/10.1007/978-3-030-12767-1_5 Acknowledgements. S.-A. N. Alexandropoulos is supported by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY).
  • Parameter estimation of Takagi–Sugeno fuzzy system using ...

    www.sciencedirect.com/science/article/pii/S0925231214014489 The implementation of iTaSuM is described in Algorithm 2.Fig. 2 is the flowchart of HeCoS-based fuzzy model. In the definition part, we define fuzzy rules, input space and handle the constrains as the pre-process. Then, we encode each nest vector follow the method of Fig. 1.The MSE value, which is calculated from T–S model output and the desired output, is used to select the best individual ...