Development of logical operator’s in common genetic programming (CGP) and its calibration in SOP rule

Common genetic programming (CGP) with considering empirical data (observed) improves and evolves estimated data (calculated). However, CGP is not able to solve multi-conditional problems with satisfactory performance. In this study, capability of the CGP is improved through development and integration of mathematical functions and logical operators on it. Proposed algorithm is called logic genetic programming (LGP) that its performance improvement is investigated in comparison with CGP in field of water resources. Results show that LGP capability, is more effective and more efficient than CGP, so that LGP improves objective function by 39 percent compared to CGP, in extraction of standard operating policy (SOP) [with minimization of mean absolute error (MAE)]. Comparison of algorithms results using the evaluation criteria indicate that LGP algorithm in SOP reconstruction resulted in a 22% decrease in RMSE and a 1% increase in NSE compared to CGP.