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Athanasios Tsakonas
Researcher at Bournemouth University
Publications - 40
Citations - 572
Athanasios Tsakonas is an academic researcher from Bournemouth University. The author has contributed to research in topics: Genetic programming & Computational intelligence. The author has an hindex of 12, co-authored 40 publications receiving 539 citations. Previous affiliations of Athanasios Tsakonas include University of the Aegean & Aristotle University of Thessaloniki.
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Journal ArticleDOI
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
TL;DR: Four context-free grammars are presented and used to describe decision trees, fuzzy rule-based systems, feedforward neural networks and fuzzy Petri-nets with genetic programming and cellular encoding is applied in order to express feedforward Neural Networks and fuzzyPetri- nets with arbitrary size and topology.
Journal ArticleDOI
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
TL;DR: The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy by means of genetic programming, and presentsicative classification results in terms of both, classification accuracy and solution interpretability.
Journal ArticleDOI
Evolving rule-based systems in two medical domains using genetic programming
Athanasios Tsakonas,Georgios Dounias,Jan Jantzen,Hubertus Axer,Beth Bjerregaard,Diedrich Graf v. Keyserlingk +5 more
TL;DR: The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.
Journal ArticleDOI
Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation
TL;DR: It was found that the prediction ability of GP on an external validation set was higher compared to that of the ANNs, with the multi population and standard GP combined with an extended function set, showing slightly better predictive performance.
Book ChapterDOI
Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
TL;DR: This paper emphasizes the appropriateness of hybrid computational intelligence techniques for dealing with specific problems, tries to point particularly suitable areas of application for different combinations of intelligent techniques and briefly state advantages and disadvantages of the "hybrid" idea.