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A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

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Author
Bérchez-Moreno, Francisco
Durán-Rosal, Antonio Manuel
Hervás Martínez, César
Gutiérrez, Pedro A.
Fernández, Juan Carlos
Publisher
Nature (Springer)
Date
2024
Subject
Artificial neural networks
Neuroevolution
Coral reef optimisation algorithm
Local search
Classification,
Robust estimators
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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.
URI
http://hdl.handle.net/10396/30831
Fuente
Bérchez-Moreno, F., Durán-Rosal, A.M., Hervás Martínez, C. et al. A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks. Sci Rep 14, 6961 (2024). https://doi.org/10.1038/s41598-024-57654-2
Versión del Editor
http://dx.doi.org/10.1038/s41598-024-57654-2
Nota
This work has been partially subsidised by “Agencia Española de Investigación (España)” (grant reference: PID2020-115454GB-C22 / AEI / 10.13039 / 501100011033), by “Investigo 2021 Programme”, funded by the European Union, through the “Plan de Recuperación, Transformación y Resiliencia (PRTR)”, and via the Anda- lusian Department of Employment, Enterprises & Self-Employed - Junta de Andalucía (grant Ref. INVEST_ SAE22_004), by Test and Experiment Facilities for the Agri-Food Domain (AgriFoodTEF). Funding body: EU Commission, DIGITAL-2022-CLOUD-AI-02. PI Spanish node: R. García-González (Univ. de Lleida) Dates: 01/01/2023 to 31/12/2027. (grant reference: 101100622). Amount granted to UCO: 742,152€ (+ 2,500,000€ from the MAPA Spanish Ministry) and by ENIA International Chair in Agriculture University of Córdoba. Funding body: MINECO (Cátedras ENIA), Spain. IP: R. Gallardo (UCO). Dates: 01/01/2023 to 31/12/2027. (grant refer- ence: TSI-100921-2023-3) Amount granted: 1,200,000€.
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