S
Spiridon D. Likothanassis
Researcher at University of Patras
Publications - 119
Citations - 1084
Spiridon D. Likothanassis is an academic researcher from University of Patras. The author has contributed to research in topics: Adaptive filter & Artificial neural network. The author has an hindex of 17, co-authored 117 publications receiving 1032 citations. Previous affiliations of Spiridon D. Likothanassis include University of the Aegean & Research Academic Computer Technology Institute.
Papers
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Journal ArticleDOI
Applying evolutionary computation to the school timetabling problem: The Greek case
Grigorios N. Beligiannis,Charalampos N. Moschopoulos,Georgios P. Kaperonis,Spiridon D. Likothanassis +3 more
TL;DR: An adaptive algorithm based on evolutionary computation techniques is designed, developed and applied to the timetabling problem of educational organizations in Greece and is able to construct a feasible and very efficient timetable more quickly and easily compared to other techniques.
Journal ArticleDOI
Exchange-Rates Forecasting: A Hybrid Algorithm Based onGenetically Optimized Adaptive Neural Networks
TL;DR: Simulation results show that the proposed algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter, in order to evolve the structure and train Multi-Layered Perceptron (MLP) neural networks, gives highly successful results.
Journal ArticleDOI
Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role
Dimitrios Kleftogiannis,Aigli Korfiati,Konstantinos Theofilatos,Spiridon D. Likothanassis,Athanasios K. Tsakalidis,Seferina Mavroudi,Seferina Mavroudi +6 more
TL;DR: The various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes.
Journal ArticleDOI
A genetic algorithm approach to school timetabling
TL;DR: The most significant contribution of the paper is that the proposed algorithm allows for criteria adaptation, thus producing different timetables for different constraints priorities, thus meeting the different needs that each school may have.
Journal ArticleDOI
Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique
TL;DR: Simulation results show that the algorithm identifies the true model and the true values of the unknown parameters for each different model structure, thus assisting the GP technique to converge more quickly to the (near) optimal model structure.