Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Genetic Programming Theory and Practice XVGenetic Programming Based on Error Decomposition: A Big Data Approach

Genetic Programming Theory and Practice XV: Genetic Programming Based on Error Decomposition: A... [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 modeling a regression problem. The results illustrate that the MSGP algorithm outperforms standard GP in terms of accuracy, efficiency, and computational cost.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Genetic Programming Theory and Practice XVGenetic Programming Based on Error Decomposition: A Big Data Approach

Part of the Genetic and Evolutionary Computation Book Series
Editors: Banzhaf, Wolfgang; Olson, Randal S.; Tozier, William; Riolo, Rick

Loading next page...
 
/lp/springer-journals/genetic-programming-theory-and-practice-xv-genetic-programming-based-uIuB3CK0Kx

References (22)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG, part of Springer Nature 2018
ISBN
978-3-319-90511-2
Pages
135 –147
DOI
10.1007/978-3-319-90512-9_9
Publisher site
See Chapter on Publisher Site

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 modeling a regression problem. The results illustrate that the MSGP algorithm outperforms standard GP in terms of accuracy, efficiency, and computational cost.]

Published: Jul 6, 2018

There are no references for this article.