Evgenia Papavasileiou

Orcid: 0000-0002-8687-2808

According to our database1, Evgenia Papavasileiou authored at least 11 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2022
Towards optimizing neural networks' connectivity and architecture simultaneously with feature selection.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021
A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies".
Evol. Comput., 2021

2020
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging.
CoRR, 2020

Behavior-based Speciation in Classification with NeuroEvolution.
Proceedings of the IEEE Congress on Evolutionary Computation, 2020

2019
Computer Aided Detection and Diagnosis System for Breast Cancer Detection Based on High Resolution 3D micro-CT Breast Microcalcifications.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

2018
Towards a CAD System for Breast Cancer Based on Individual Microcalcifications?
Proceedings of the 20th IEEE International Conference on e-Health Networking, 2018

Configuring the parameters of artificial neural networks using neuroevoiution and automatic algorithm configuration.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.
Medical Image Anal., 2017

The importance of the activation function in NeuroEvolution with FS-NEAT and FD-NEAT.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

An investigation of topological choices in FS-NEAT and FD-NEAT on XOR-based problems of increased complexity.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016
A comparison between FS-NEAT and FD-NEAT and an investigation of different initial topologies for a classification task with irrelevant features.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016


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