## 2 Methods
Operations are recursively applied to the new generations until a stopping criterion is reached. The recursion stops when some individual program reaches a predefined fitness level or when the process reaches a given number of generations (_Ng_). The output of this process consists of the best individual function from all generations. In our case, we generated a first random population of 70,000 functions, and selected the best 10,000 individuals according to the obtained mean square error. We set a maximum number of 100 generations as the termination criterion. In this study, we implemented GP to generate composite indicators that capture the optimal combinations of survey variables that best track the actual evolution of economic growth. Formally, the objective of the algorithm is to infer a functional relationship from a set of observations, such that the inferred function \(f(x_{i})\) is as near as possible to the reference series in the Euclidean distance sense, where index \(i=1,\dots,\ M\) denotes the sample size. The search process is characterized by a trade-off between accuracy and simplicity. To limit the complexity of the resulting expressions, the set of functions is restricted to the four elementary mathematical operations (addition, subtraction, product, and division). One can refer to [43] for a detailed study on the effect of the choice of function sets on the generalization performance of SR models. With the aim of further restricting the complexity of the resulting functional forms, we additionally introduced regularization terms in the slope and curvature of the inferred functions. For a justification of the need to regularize, one can refer to [44]. We used the Distributed Evolutionary Algorithms in Python (DEAP), developed by [45]. Finally, by way of synthesis, we want to highlight some of the advantages and disadvantages of the proposed methodology. As for the advantages, we want to note that, unlike the confidence indicators constructed by the EC, the obtained evolved expressions proposed in the present study generate direct estimates of economic growth, which are based on information from all the available variables of the industry and consumer surveys. Additionally, the proposed approach automatically selects the expectational variables with the highest forecasting power and their optimal lag structure, which is predefined in the design of the experiment, ranging in our case from one to a maximum of four quarters. Finally, the proposed approach detects existing non-linear relationships between survey expectations and allows them to be modelled. Regarding the limitations of the proposed methodology, we want to note that due to the empirical nature of the proposed approach, the evolved expressions lack any theoretical background. Another limitation of the proposed approach is that, as opposed to standard regression, the significance of the parameters obtained in SR cannot be assessed. Finally, given that GP-based SR is a stochastic, high-variance algorithm, its sensitivity to changes in training data can be a drawback for certain practical applications. In this sense, the implementation of preliminary sensitivity analyses through Monte Carlo simulations and the incorporation of prior knowledge are ways to mitigate this effect.