GRADIENT: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems

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@Article{Tsakonas2012,

• author = "Athanasios Tsakonas and Bogdan Gabrys",
• title = "{GRADIENT}: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems",
• journal = "Expert Systems with Applications",
• volume = "39",
• number = "18",
• pages = "13253--13266",
• year = "2012",
• month = "15 " # dec,
• keywords = "genetic algorithms, genetic programming, Multi-level prediction systems, Ensemble systems, Function approximation, Non-linear regression",
• ISSN = "0957-4174",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417412007920",
• DOI = "doi:10.1016/j.eswa.2012.05.076",
• size = "14 pages",
• abstract = "This work presents the GRADIENT (GRAmmar-DrIven ENsemble sysTem) framework for the generation of hybrid multi-level predictors for function approximation and regression analysis tasks. The proposed model uses a context-free grammar guided genetic programming for the automatic building of multi-component prediction systems with hierarchical structures. A multi-population evolutionary algorithm together with resampling and cross-validatory approaches are used to increase component models' diversity and facilitate more robust and efficient search for accurate solutions. The system has been tested on a number of synthetic and publicly available real-world regression and time series problems for a range of configurations in order to identify and subsequently illustrate and discuss its characteristics and performance. GRADIENT has been shown to be very competitive and versatile when compared to a number of state-of-the-art prediction methods.",

}

Genetic Programming entries for Athanasios D Tsakonas Bogdan Gabrys

Citations