Multiobjective genetic programming for reinforced concrete beam modeling. Issue 1 (29th September 2020)

• Record Type: Journal Article
• Authors: Tahmassebi, Amirhessam; Mohebali, Behshad; Meyer‐Baese, Anke; Gandomi, Amir H.
• Is Part Of: Applied AI Letters. Volume 1:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-29
• Subjects: multiobjective genetic programming -- RC beam -- symbolic regression; 006.3
• DOI: 10.1002/ail2.9
• Languages: English
• ISSNs: 2689-5595

Abstract

Abstract: This paper presents the application of multiobjective genetic programming (MOGP) in engineering issues. An evolutionary symbolic implementation was developed based on a case study on prediction of the shear strength of slender reinforced concrete beams without stirrups including 1942 set of published test results. In the implementation of the MOGP model, the nondominated sorting genetic algorithm II with adaptive regression by mixing algorithm with considering the optimization of mean‐square error as the fitness measure and the subtree complexity was used. The developed MOGP model was compared to previously developed genetic programming models, different building codes, and additional machine learning based approaches. It is clearly shown that the MOGP model outperformed the other algorithms applied on this database and can be a general solution on any engineering problems with the main advantage of prediction equations without assuming prior form of the relevance among the input predictor variables.