For the realization of complex renewable energy systems (such as nano-fluids based direct absorption solar collector), an evolutionary system identification method such as genetic programming (GP) can be applied to develop mathematical models/functional relationships between the process parameters. The system complexity is attributed to interaction among the design variables influencing the outputs. There are also uncertainties in the system due to random and unknown variations in the design and response variables. GP suffers from the higher complexity structure of its solutions and non-optimal convergence, which leads to poor fitness values. Therefore, to address these uncertainties and problems, the framework based on the model selection criteria approximated genetic programming (MSC-GP) is proposed for the formulation of geometry design based thermal efficiency and entropy generation optimization function for direct absorption solar collector (DASC) system. In this proposed method, the four mathematical model selection criteria are used as an approximation for objective functions in GP framework for the evaluation of fitting degree and structure of the model. The results based on statistical measures (best fitness, mean fitness, standard deviation of fitness, number of nodes) show that models obtained from the mathematical selection criteria, Predicted Residual error sum of squares (PRESS), have performed the best. Based on Pareto front analysis of PRESS function, it is found that the best objective values and the number of nodes of models (complexity) follows more or less gradually slow increasing trend which is a good symbolic desirable sign of minimal increase of complexity of model with a decrease in objective values as the values of generation increases. The results of the sensitivity analysis show that the main factor affecting the efficiency of DASC is its geometry of the structure. 3-D interaction analysis shows that increasing the thickness, length and reducing the width of the collector can make the system maintain its higher thermal efficiency and a smaller entropy generation, which is useful for the optimized operation of DASC. Non-dominated sorting genetic algorithm-II (NSGA-II) is applied in the acquisition of the optimal geometric settings of DASC system based on the selected models. The optimal settings achieved is 5 cm in length, 5 cm in width, and 2 cm in thickness. Systems when operated using these settings results in a satisfactory performance with 77.8117% in thermal efficiency and 6.0004E+3 in entropy generation).

为了实现复杂的可再生能源系统（例如基于纳米流体的直接吸收式太阳能收集器），可以应用进化系统识别方法（例如遗传程序设计（GP））来开发过程参数之间的数学模型/功能关系。系统复杂性归因于影响输出的设计变量之间的相互作用。由于设计和响应变量的随机和未知变化，系统中也存在不确定性。GP受其解决方案的较高复杂性结构和非最佳收敛的影响，这导致适用性较差。因此，为了解决这些不确定性和问题，提出了一种基于模型选择标准的近似遗传规划框架（MSC-GP），用于基于几何设计的直接吸收式太阳能集热器（DASC）系统的热效率和熵产生优化函数的制定。在该方法中，将四个数学模型选择标准用作GP框架中目标函数的近似值，以评估模型的拟合程度和结构。基于统计量度（最佳适应度，平均适应度，适应度的标准偏差，节点数）的结果表明，从数学选择标准，预测残差平方和（PRESS）获得的模型表现最佳。根据PRESS函数的Pareto前沿分析，发现最佳目标值和模型的节点数（复杂性）或多或少地遵循逐渐缓慢的增长趋势，这是模型复杂度最小增加而目标值减小的最好的象征性期望标志，即代数增加。灵敏度分析的结果表明，影响DASC效率的主要因素是其结构的几何形状。3-D相互作用分析表明，增加收集器的厚度，长度和减小收集器的宽度可以使系统保持较高的热效率和较小的熵产生，这对于DASC的优化运行非常有用。非主导排序遗传算法-II（NSGA-II）用于基于所选模型的DASC系统的最佳几何设置的获取。达到的最佳设置是长5厘米，宽5厘米，厚2厘米。当使用这些设置进行操作时，系统具有令人满意的性能，热效率为77.8117％，熵产生为6.0004E + 3。