Title A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks
Authors Bérchez-Moreno F. , DURAN ROSAL, ANTONIO MANUEL, Hervás Martínez C. , Gutiérrez P.A. , Fernández J.C.
External publication No
Means Scientific Reports
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188527037&doi=10.1038%2fs41598-024-57654-2&partnerID=40&md5=229db6235793bc21614b70f70934f82a
Publication date 01/01/2024
ISI 001190086900045
Scopus Id 2-s2.0-85188527037
DOI 10.1038/s41598-024-57654-2
Abstract Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance. © The Author(s) 2024.
Keywords Artificial neural networks; Classification; Coral reef optimisation algorithm; Local search; Neuroevolution; Robust estimators
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