1. Title of the Paper Developing an Alternative Calculation Method for the Smart Readiness Indicator Based on Genetic Programming and Linear Regression 2. Authors and Contact Information Mitja Beras Feniks Pro d.o.o., Ljubljana, Slovenia & PhD Candidate, Faculty of Mechanical Engineering, University of Maribor, Slovenia mitja.beras@gmail.com | +386 31 000 000 Miran Brezočnik Faculty of Mechanical Engineering, University of Maribor, Slovenia miran.brezocnik@um.si Uroš Župerl Faculty of Mechanical Engineering, University of Maribor, Slovenia uros.zuperl@um.si Miha Kovačič Štore Steel d.o.o., Slovenia Faculty of Mechanical Engineering, University of Ljubljana miha.kovacic@store-steel.si 3. Corresponding Author Mitja Beras 4. Abstract Abstract: The European Union is planning to introduce a new tool for evaluating smart solutions in buildings—the Smart Readiness Indicator (SRI). As 54 energy effi ciency cate- gories must be evaluated, the triage process can be long and time-intensive. Altogether, 228 data points (or inputs) about the smartness of the buildings are required to complete the evaluation. The present paper proposes an alternative calculation method based on genetic programming (GP) for the calculation of Domains and linear regression (LR) for the calculation of Impact Factors and the total SRI score of the building. This novel calcula- tion requires 20% (Domain ventilation and dynamic building envelope) to 75% (Domain cooling) fewer inputs than the original methodology. The present study evaluated 223 case study buildings, and 7 genetic programming models and 8 linear regression models were generated based on the results. The generated results are precise; the relative deviation from the experimental data for Domain scores (modelled with GP) ranged from 0.9% to 2.9%. The R 2 for the LR models was 0.75 for most models (with two exceptions, with one with a value of 0.57 and the other with a value of 0.98). The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments. 5. Claimed Criteria for Human-Competitiveness B, D, E, G 6. Justification of Human-Competitiveness This paper addresses a real-world EU policy implementation issue — the Smart Readiness Indicator (SRI) — by proposing a computational method that reduces complexity and input requirements while maintaining high accuracy. The developed method: Outperforms the standard in usability by reducing input data by up to 75% (see Table 7, page 22)buildings-15-01675-v2. Is validated across 223 case study buildings and further tested on an external dataset (20 test buidlings), proving generalizability and scalability. Represents a novel scientific contribution, being the first method to combine genetic programming (GP) and linear regression (LR) to model SRI scores, with high fidelity (R² values up to 0.99, see Table 4, page 14) buildings-15-01675-v2. Provides actionable insights to stakeholders across the EU — an urgent policy domain affecting millions of buildings under the Green Deal, and EPBD frameworks. Udner the Renovation Wave cca. 30 mio across EU have to be evaluated under the SRI methodology/framework. 7. Full Citation Beras, M.; Brezočnik, M.; Župerl, U.; Kovačič, M. (2025). Developing an Alternative Calculation Method for the Smart Readiness Indicator Based on Genetic Programming and Linear Regression. Buildings, 15(10), 1675. https://doi.org/10.3390/buildings15101675 8. Prize Money Division Equal distribution among all four co-authors (25% each) 9. Why the Entry Could Be the Best This work provides a rare fusion of cutting-edge AI techniques and real-world energy policy application, delivering measurable benefits in performance, scalability, and implementation feasibility. It bridges a gap in the literature and policy by operationalizing SRI calculations through automation, reducing barriers to entry for thousands of EU building operators. Its impact spans scientific, technical, and policy domains. The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments. 10. Type of Evolutionary Computation Used GP (Genetic Programming) LR (Linear Regression — used in combination for hybrid modelling) 11. Date of Publication May 15, 2025 (Published in Buildings, MDPI)