Improving the Generalization Abilities of Constructed Neural Networks with the Addition of Local Optimization Techniques

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@Article{tsoulos:2024:Algorithms,

• author = "Ioannis G. Tsoulos and Vasileios Charilogis and Dimitrios Tsalikakis and Alexandros Tzallas",
• title = "Improving the Generalization Abilities of Constructed Neural Networks with the Addition of Local Optimization Techniques",
• journal = "Algorithms",
• year = "2024",
• volume = "17",
• number = "10",
• pages = "Article No. 446",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN",
• ISSN = "1999-4893",
• URL = "https://www.mdpi.com/1999-4893/17/10/446",
• DOI = "doi:10.3390/a17100446",
• abstract = "Constructed neural networks with the assistance of grammatical evolution have been widely used in a series of classification and data-fitting problems recently. Application areas of this innovative machine learning technique include solving differential equations, autism screening, and measuring motor function in Parkinson's disease. Although this technique has given excellent results, in many cases, it is trapped in local minimum and cannot perform satisfactorily in many problems. For this purpose, it is considered necessary to find techniques to avoid local minima, and one technique is the periodic application of local minimization techniques that will adjust the parameters of the constructed artificial neural network while maintaining the already existing architecture created by grammatical evolution. The periodic application of local minimization techniques has shown a significant reduction in both classification and data-fitting problems found in the relevant literature.",
• notes = "also known as \cite{a17100446}",

}

Genetic Programming entries for Ioannis G Tsoulos Vasileios Charilogis Dimitrios Tsalikakis Alexandros T Tzallas

Citations