K
Kyriakos Kentzoglanakis
Researcher at University of Portsmouth
Publications - 6
Citations - 123
Kyriakos Kentzoglanakis is an academic researcher from University of Portsmouth. The author has contributed to research in topics: Particle swarm optimization & Swarm intelligence. The author has an hindex of 3, co-authored 6 publications receiving 112 citations.
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Journal ArticleDOI
A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures
TL;DR: Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.
Proceedings ArticleDOI
Particle swarm optimization with an oscillating inertia weight
TL;DR: Results demonstrate that an oscillating inertia weight function is competitive and in some cases better than established inertia weight functions, in terms of consistency and speed of convergence.
Book ChapterDOI
Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series
TL;DR: This paper implements an ant system to generate candidate network structures using a particle swarm optimization algorithm, and extends this approach by incorporating domain-specific heuristics to the ant system, as a mechanism that has the potential to bias the pheromone amplification effect towards biologically plausible relationships.
Journal ArticleDOI
VAR model training using particle swarm optimisation: evidence from macro-finance data
TL;DR: In this article, the authors examined the empirical relationship between CPI, oil prices, stock market and unemployment in EU15 using a new computational approach and proposed a novel approach to train the well-known vector autoregressive (VAR) model using a particle swarm optimisation (PSO) method.
Gene network inference using a swarm intelligence framework
TL;DR: In this paper, a model-based approach is adopted, according to which the quality of a candidate architecture is evaluated by assessing the ability of the corresponding trained model to reproduce the available dynamics.