This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data., {"references":["Funke, H.; Gelbrich, S.; Ehrlich, A.: Development of a new hybrid material of textile reinforced concrete and glass fibre reinforced plastic. In: Hufenbach, W. A.; Gude, M.: Procedia Materials Science. Materials Science Engineering, Symposium B6 - Hybrid Structures. Volume 2, ISSN 2211-8128, pp. 103-110.","Funke, H.; Gelbrich, S.; Ehrlich, A.; Kroll, L.: A Fiber-Reinforced Architectural Concrete for the Newly Designed Façade of the Poseidon Building in Frankfurt am Main. Journal of Materials Science Research. Volume 3, No. 3 (2014), doi:10.5539/jmsr.v3n3p33, ISSN 1927-0585, pp. 33-39.","Funke, H.; Gelbrich, S.; Ehrlich, A.; Ulke-Winter, L.; Kroll, L.: Unsymmetrical Fibre-Reinforced Plastics for the Production of Curved Textile Reinforced Concrete Elements. In: Open Journal of Composite Materials, 2014, 4, doi: 10.4236/ojcm.2014.44021, ISSN: 2164-5655 (online), 2164-5612 (print), pp. 191-200.","McCulloch, W. S.; Pitts, W.: A logical calculus of the idea immanent in nervous activity. Bulletin of Mathematical Biophysics, Vol. 5 (1943), pp. 115-133.","Budelmann, H., Rostasy, F.S., Hariri, K., Holst, A.; Wichmann, H.-J.: Zustandserfassung und -beurteilung vorgespannter Zugglieder durch Monitoring, Tagungsband Berichtskolloquium des SFB 477, 16./17. Juni 2003, pp. 69-77.","Adeli, H.: Neural networks in civil engineering 1989-2000. Computer-Aided Civil and Infrastructure Engineering 16 (2001), pp. 126–142.","Hola, J.; Schabowicz, K.: Beurteilung der Betonfestigkeit unter Nutzung der künstlichen Neuronalen Netze aufgrund zerstörungsfreier Untersuchungen. Z. Beton- und Stahlbetonbau. 100. (2005), Nr. 5, pp. 416-421.","Freitag, S.; Graf, W.; Kaliske, M.: Prognose des Langzeitverhaltens von Textilbeton-Tragwerken mit rekurrenten neuronalen Netzen. In: Curbach, M. (Hrsg.): CTRS4 – 4. Kolloquium zu textilbewehrten Tragwerken. Technische Universität Dresden (2009), pp. 365-376.","Bergmeister, K.; Santa, U.; Strauss, A.: Überwachung und Analyse der Lebensdauer von Tunnelbauwerken. Z. Beton- und Stahlbetonbau. 102 (2007), Nr. 1, pp. 24-32.\n[10]\tFerreira C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 2001;13(2), pp. 87–129.\n[11]\tGoldberg DE. Genetic algorithms in search, optimization, and machine learning. Addisson Wesley; 1989.\n[12]\tHolland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. A Bradford Book; 1992.\n[13]\tAdams RD, Maheri MR. Dynamic flexural properties of anisotropic fibrous composite beams. Compos Sci Technol 1994;50(4), pp. 497–514.\n[14]\tUlke-Winter, L.: Naturanaloge Optimierungsverfahren zur Auslegung von Faserverbund-strukturen. Universitätsverlag Chemnitz; Dissertation; 2017."]}