Genetic Programming Based on Error Decomposition: A Big Data Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Tahmassebi:2017:GPTP,

• author = "Amirhessam Tahmassebi and Amir H. Gandomi",
• title = "Genetic Programming Based on Error Decomposition: A Big Data Approach",
• booktitle = "Genetic Programming Theory and Practice XV",
• editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo",
• year = "2017",
• series = "Genetic and Evolutionary Computation",
• pages = "135--147",
• address = "University of Michigan in Ann Arbor, USA",
• month = may # " 18--20",
• organisation = "the Center for the Study of Complex Systems",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-90511-2",
• URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_9",
• DOI = "doi:10.1007/978-3-319-90512-9_9",
• abstract = "An investigation of the deviations of error and correlation for different stages of the multi-stage genetic programming (MSGP) algorithm in multivariate nonlinear problems is presented. The MSGP algorithm consists of two main stages: (1) incorporating the individual effect of the predictor variables, (2) incorporating the interactions among the predictor variables. The MSGP algorithm formulates these two terms in an efficient procedure to optimize the error among the predicted and the actual values. In addition to this, the proposed pipeline of the MSGP algorithm is implemented with a combination of parallel processing algorithms to run multiple jobs at the same time. To demonstrate the capabilities of the MSGP, its performance is compared with standard GP in modelling a regression problem. The results illustrate that the MSGP algorithm outperforms standard GP in terms of accuracy, efficiency, and computational cost.",
• notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018",

}

Genetic Programming entries for Amirhessam Tahmassebi A H Gandomi

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