A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming

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@InProceedings{Casadei:2019:BRACIS,

• author = "Felipe Casadei and Joao Francisco B. S. Martins and Gisele L. Pappa",
• title = "A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming",
• booktitle = "2019 8th Brazilian Conference on Intelligent Systems (BRACIS)",
• year = "2019",
• pages = "66--71",
• month = "15-18 " # oct,
• address = "Salvador, Brazil",
• keywords = "genetic algorithms, genetic programming, Geometric Semantic Genetic Programming, Symbolic Regression, Training, Semantic search, Sociology, Statistics, Predictive models, Multi-ojective Optimization",
• isbn13 = "978-1-7281-4254-8",
• ISSN = "2643-6264",
• DOI = "doi:10.1109/BRACIS.2019.00021",
• size = "6 pages",
• abstract = "This paper proposes a multi-objective approach for solving symbolic regression problems using Geometric Semantic Genetic Programming (GSGP). The proposed method produces models specialized in smaller regions of the semantic search space, where the errors of the models into these different regions are the objectives being optimized. The method incorporates different ways of defining these sub-regions of the semantic space as well as a method to combine the models found intending to produce a unique prediction. Experimental results obtained over 10 real-world datasets show that the proposed method outperforms traditional GSGP in 7 out of 10 datasets.",
• notes = "Also known as \cite{8923924}",

}

Genetic Programming entries for Felipe Casadei Joao Francisco B S Martins Gisele L Pappa

Citations