@inproceedings{b332fc0981244efabe41448121ea9f67,
title = "Parametric learning of associative functional networks through a modified memetic self-adaptive firefly algorithm",
abstract = "Functional networks are a powerful extension of neural networks where the scalar weights are replaced by neural functions. This paper concerns the problem of parametric learning of the associative model, a functional network that represents the associativity operator. This problem can be formulated as a nonlinear continuous least-squares minimization problem, solved by applying a swarm intelligence approach based on a modified memetic self-adaptive version of the firefly algorithm. The performance of our approach is discussed through an illustrative example. It shows that our method can be successfully applied to solve the parametric learning of functional networks with unknown functions.",
keywords = "Artificial intelligence, Associative model, Firefly algorithm, Functional networks, Parametric learning, Swarm intelligence",
author = "Akemi G{\'a}lvez and Andr{\'e}s Iglesias and Eneko Osaba and \{Del Ser\}, Javier",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",
year = "2020",
doi = "10.1007/978-3-030-50426-7\_42",
language = "English",
isbn = "9783030504250",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "566--579",
editor = "Krzhizhanovskaya, \{Valeria V.\} and G{\'a}bor Z{\'a}vodszky and Lees, \{Michael H.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Sloot, \{Peter M.A.\} and Dongarra, \{Jack J.\} and S{\'e}rgio Brissos and Jo{\~a}o Teixeira",
booktitle = "Computational Science – ICCS 2020 - 20th International Conference, Proceedings",
address = "Germany",
}