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\begin{document}

\title{Multi-Objective Genetic Programming for RC Beam Modeling}



\author[1]{Amirhessam  Tahmassebi}%
\author[1]{Behshad  Mohebali}%
\author[1]{Anke  Meyer-Baese}%
\author[2]{Amir Gandomi}%
\affil[1]{Florida State University}%
\affil[2]{University of Technology Sydney}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
This paper presents the application of multi-objective genetic
programming 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 non-dominated 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 GP models, different building codes, and additional machine
learning-based approaches. It is clearly shown that the MOGP model
outperformed the other algorithms applied in this database and can be a
general solution to any engineering problems with the main advantage of
prediction equations without assuming the prior form of the relevance
among the input predictor variables.%
\end{abstract}%



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