General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution

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@Article{journals/isci/Estevez-Velarde21,

• author = "Suilan Estevez-Velarde and Yoan Gutierrez and Yudivian Almeida-Cruz and Andres Montoyo",
• title = "General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution",
• journal = "Information Sciences",
• year = "2021",
• volume = "543",
• pages = "58--71",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, AutoML, evolutionary computation, supervised learning, natural language processing",
• ISSN = "0020-0255",
• bibdate = "2020-11-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isci/isci543.html#Estevez-Velarde21",
• URL = "https://www.sciencedirect.com/science/article/pii/S0020025520306988",
• DOI = "doi:10.1016/j.ins.2020.07.035",
• abstract = "This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community.",

}

Genetic Programming entries for Suilan Estevez-Velarde Yoan Gutierrez Yudivian Almeida-Cruz Andres Montoyo

Citations