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GRADIENT: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems
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DOI：10.1016/j.eswa.2012.05.076.png)
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Abstract
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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 multicomponent 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. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Multi-level prediction systems
Ensemble systems
Function approximation
Grammar-driven genetic programming
Non-linear regression
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