Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/175915
Título: Exploring Geometric Semantic Genetic Programming for the evolution of Classifiers [poster]
Autor: Rasteiro, Diogo
Data: 27-Set-2024
Resumo: Genetic Programming (GP) is the concept of building and evolving computer programs according to the Darwin's Theory of Evolution. Many different algorithms fall under its umbrella, and this document takes particular interest in one called Geometric Semantic Genetic Programming (GSGP). This specific variation uses an approach focused on the semantics of the evolved computer programs and utilizes genetic operators with specific properties that allow them to induce a uni-modal fitness landscape on any type of problem. The results displayed by this technique are excellent, particular within the realm of solving regression problems. The aim of this work is to explore the potential of GSGP techniques, particularly in the realm of classification problems and aims to develop implementations specifically designed for multi-class problems.
Descrição: Rasteiro, D. (2024). Exploring Geometric Semantic Genetic Programming for the evolution of Classifiers [poster]. 1. Poster session presented at Data Research Meetup by MagIC, Lisbon, Portugal. --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
Peer review: no
URI: http://hdl.handle.net/10362/175915
Aparece nas colecções:NIMS: MagIC - Documentos de conferências nacionais

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