{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T06:50:32Z","timestamp":1768891832913,"version":"3.49.0"},"reference-count":83,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["TAEDK-06195"],"award-info":[{"award-number":["TAEDK-06195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological\/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (&gt;35 \u00b0C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike \u201cblack-box\u201d machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion.<\/jats:p>","DOI":"10.3390\/bdcc9060142","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T08:49:57Z","timestamp":1747903797000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting the Damage of Urban Fires with Grammatical Evolution"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9895-8880","authenticated-orcid":false,"given":"Constantina","family":"Kopitsa","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-2733","authenticated-orcid":false,"given":"Ioannis G.","family":"Tsoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-9088","authenticated-orcid":false,"given":"Andreas","family":"Miltiadous","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, Greece"}]},{"given":"Vasileios","family":"Charilogis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, Kostaki Artas, 47150 Artas, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","unstructured":"(2025, March 07). Fires\u2014Wildfires and Urban Fires. Juniata County Appendix CMulti-Jurisdictional Hazard Mitigation Plan Hazard Profiles. Available online: https:\/\/juniataco.org\/docs\/hmp\/Appendix%20C%20-%2004-Fire-Urban%20and%20Rural.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102863","DOI":"10.1016\/j.apgeog.2022.102863","article-title":"Analyzing the risk factors of residential fires in urban and rural census tracts of Ohio using panel data analysis","volume":"151","author":"Hossain","year":"2023","journal-title":"Appl. Geogr."},{"key":"ref_3","unstructured":"Hellenic Fire Service (2025, March 07). Open Data. Incident Record. Available online: https:\/\/www.fireservice.gr\/el_GR\/synola-dedomenon."},{"key":"ref_4","unstructured":"Greek Wikipedia (2025, February 27). The Trial Regarding MATI\u2019s Wildfire. Available online: https:\/\/el.wikipedia.org\/wiki\/%CE%94%CE%AF%CE%BA%CE%B7_%CE%B3%CE%B9%CE%B1_%CF%84%CE%BF_%CE%9C%CE%AC%CF%84%CE%B9."},{"key":"ref_5","unstructured":"Xanthopoulos, G., and Athanasiou, M. (2019). Uniting Our Global Wildfire Community, Wildfire, International Association of Wildland Fire."},{"key":"ref_6","unstructured":"World Health Organization (WHO) (2025, March 13). Burns. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/burns."},{"key":"ref_7","unstructured":"Natural Hazards Research Australia (2025, March 13). Understanding the Black Summer Bushfires Through Research: A Summary of Key Finding from the Bushfire and Natural Hazards CRC. Available online: https:\/\/www.naturalhazards.com.au\/sites\/default\/files\/2023-01\/Understanding%20the%20Black%20Summer%20bushfires%20through%20research_final_web_NHRA.pdf."},{"key":"ref_8","unstructured":"Australian Government, and Australian Public Service Commission (2025, March 13). Black Summer. State of the Service Report 2019\u201320, Available online: https:\/\/www.apsc.gov.au\/state-service\/state-service-report-2019-20\/chapter-1-commitment-service\/black-summer."},{"key":"ref_9","unstructured":"NASA Earth Observatory (2025, March 17). Fires Char the Siberian Arctic, Available online: https:\/\/earthobservatory.nasa.gov\/images\/153087\/fires-char-the-siberian-arctic."},{"key":"ref_10","unstructured":"NASA (2025, March 17). Landsat Image Gallery, Available online: https:\/\/landsat.visibleearth.nasa.gov\/view.php?id=153087."},{"key":"ref_11","unstructured":"Latypova, L. (2025, March 17). Raging Wildfires Devastate Russia\u2019s Far East Sakha Republic. The Moscow Times, Available online: https:\/\/www.themoscowtimes.com\/2024\/07\/23\/raging-wildfires-devastate-russias-far-east-sakha-republic-a85802."},{"key":"ref_12","unstructured":"Sommer, L. (2025, March 17). Here\u2019s How Climate Change Fueled the Los Angeles Fires. National Public Radio, Available online: https:\/\/www.npr.org\/2025\/01\/29\/nx-s1-5273676\/la-fires-climate-change-rainfall-extreme-weather."},{"key":"ref_13","unstructured":"McCarthy, J., and Richter, J. (2025, March 17). Graphics Explain Los Angeles. Rare and Devastating January Fires. World Resources Institute. Wri org., Available online: https:\/\/www.wri.org\/insights\/los-angeles-fires-january-2025-explained."},{"key":"ref_14","unstructured":"NASA Earth Observatory (2025, March 17). Fire Grows Unusually Large in Japan, Available online: https:\/\/earthobservatory.nasa.gov\/images\/154008\/fire-grows-unusually-large-in-japan."},{"key":"ref_15","unstructured":"Keun-tae, P. (2025, March 17). Cities Face Rising Fire Risks from Climate Change Without Emission Cuts. ChosunBiz, Available online: https:\/\/biz.chosun.com\/en\/en-science\/2025\/03\/05\/FXRLKFRXJJB5LK4YXKPLVETVJM\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.firesaf.2013.01.024","article-title":"Exploratory and inferential methods for spatio-temporal analysis of residential fire clustering in urban areas","volume":"58","author":"Ceyhan","year":"2013","journal-title":"Fire Saf. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jsr.2012.03.003","article-title":"Reduced frequency and severity of residential fires following delivery of fire prevention education by on-duty fire fighters: Cluster randomized controlled study","volume":"43","author":"Clare","year":"2012","journal-title":"J. Saf. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alkis, S., Aksoy, E., and Akpinar, K. (2021). Risk Assessment of Industrial Fires for Surrounding Vulnerable Facilities Using a Multi-Criteria Decision Support Approach and GIS. Fire, 4.","DOI":"10.3390\/fire4030053"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Lv, A., Yan, Z., and Yang, Z. (2021). A GIS-Based Multi-Criterion Decision-Making Method to Select City Fire Brigade: A Case Study of Wuhan, China. Int. J. Geo-Inf. ISPRS, 10.","DOI":"10.3390\/ijgi10110777"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Noori, S., Mohammadi, A., Ferreira, T., Miguel, G., Gilandeh, A., Ghaffari, M., Ardabili, S., and Seyed, J. (2023). Modelling and Mapping Urban Vulnerability Index against Potential Structural Fire-related Risks: An Integrated GIS-MCDM Approach. Fire, 6.","DOI":"10.3390\/fire6030107"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1007\/s11069-020-04057-x","article-title":"Evaluating the severity of building fires with the analytical hierarchy process, big data analysis, and remote sensing","volume":"103","author":"Lee","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pamu\u010dar, D., Ecer, F., Cirovic, G., and Arlasheedi, M.A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8.","DOI":"10.3390\/math8081342"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3390\/encyclopedia3010006","article-title":"Multi-criteria decision making (MCDM) methods and concepts","volume":"3","author":"Taherdoost","year":"2023","journal-title":"Encyclopedia"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11069-020-04348-3","article-title":"Comparative evaluation of GIS-based best\u2014Worst method (BMW) for emergency facility planning: Perspectives from two decision-maker groups","volume":"105","author":"Nyimbili","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10708-024-11137-z","article-title":"Geographic patterns of urban fires in the global south: The case of Kathmandu, Nepal","volume":"89","author":"KC","year":"2024","journal-title":"GeoJournal"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1177\/8755293019878184","article-title":"A stochastic model for time series prediction of the number of post\u2014Earthquake fire ignition in buildings based on the ignition record for the 2011 Tohoku Earthquake","volume":"36","author":"Nishino","year":"2019","journal-title":"Earthq. Spectra"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s00477-019-01649-3","article-title":"Physics-based urban fires spread simulation coupled with stochastic occurrence of spot fires","volume":"33","author":"Nishino","year":"2019","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1007\/s11069-022-05802-0","article-title":"Probabilistic urban cascading multi-hazard risk assessment methodology for ground shaking and post-earthquake fires","volume":"116","author":"Nishino","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.firesaf.2013.07.002","article-title":"Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature","volume":"62","author":"Jennings","year":"2013","journal-title":"Fire Saf. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compenvurbsys.2009.09.001","article-title":"Spatial forecasting of residential urban fires: A Bayesian approach","volume":"34","author":"Rohde","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s13753-018-0209-2","article-title":"Modeling Spatial-Temporal Dynamic of Urban Residential Fire Risk Using a Markov Chain Technique","volume":"10","author":"Ardianto","year":"2019","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Suthaharan, S. (2016). Support vector machine. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Springer.","DOI":"10.1007\/978-1-4899-7641-3"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Maniatis, Y., Doganis, A., and Chatzigeorgiadis, M. (2022). Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece. Appl. Sci., 12.","DOI":"10.3390\/app12062938"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dey, A., Heger, A., and England, D. (2021). Urban Fire Station Planning using Predicted Demand and Service Quality Index, Springer Nature.","DOI":"10.1007\/s41060-022-00328-x"},{"key":"ref_38","unstructured":"Walia, B.S., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018, January 19\u201323). A Dynamic pipeline for Spatio-Temporal Fire Risk Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106730","DOI":"10.1016\/j.asoc.2020.106730","article-title":"Urban Fire Situation Forecasting: Deep sequence learning with Spatio\u2013temporal dynamics","volume":"97","author":"Jin","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1007\/s42452-025-06703-0","article-title":"Predicting firefighting operation time in urban areas using machine learning: Identifying key determinants for improved emergency response","volume":"7","author":"Sahebi","year":"2025","journal-title":"Discov. Appl. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Wylie, A.G. (2024). Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13050149"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104331","DOI":"10.1016\/j.firesaf.2024.104331","article-title":"A deep neural network approach for regional-scale 30-day accumulated urban fire occurrence forecast","volume":"152","author":"Zhou","year":"2025","journal-title":"Fire Saf. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"110080","DOI":"10.1016\/j.jobe.2024.110080","article-title":"An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings","volume":"95","author":"Liu","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_44","first-page":"100645","article-title":"Machine Learning Based Risk Analysis and Predictive Modeling of Structure Fire Related Casualties","volume":"20","author":"Schmidt","year":"2025","journal-title":"Mach. Learn. Appl."},{"key":"ref_45","first-page":"3241","article-title":"Fire Risk Prediction Analysis Using Machine Learning Techniques","volume":"35","author":"Seo","year":"2023","journal-title":"Sens. Mater."},{"key":"ref_46","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"1967","author":"MacQueen","year":"1967","journal-title":"Berkeley Symp. Math. Statist. Prob."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/S1007-0214(08)70184-6","article-title":"Urban Fire Risk Clustering Method Based on Fire Statistics","volume":"13","author":"Lizhi","year":"2008","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ishola, A.A., and Valles, D. (2023). Enhancing safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit. Sensors, 23.","DOI":"10.3390\/s23104628"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Di Martino, T., Le Saux, B., Guinvarc\u2019h, R., Thirion-Lefevre, L., and Colin, E. (2023). Detection of forest fires through deep unsupervised learning modeling of Sentinel-1 time series. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12080332"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"\u00c7ift\u00e7io\u011flu, A.\u00d6., and Naser, M.Z. (2022, January 2\u20133). Unsupervised Machine Learning for Fire Resistance Analysis. Proceedings of the International Conference on Science, Engineering Management and Information Technology, Ankara, Turkey.","DOI":"10.1007\/978-3-031-40395-8_15"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rahimi, I., Duarte, L., and Teodoro, A.C. (2025, January 1\u20133). Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection. Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), Porto, Portugal.","DOI":"10.5220\/0013201800003935"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/4235.942529","article-title":"Grammatical evolution","volume":"5","author":"Ryan","year":"2001","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bishop, C. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.protcy.2013.12.159","article-title":"The effect of data pre-processing on optimized training of artificial neural networks","volume":"11","author":"Nawi","year":"2013","journal-title":"Procedia Technol."},{"key":"ref_56","unstructured":"Backus, J.W. (1959, January 15\u201320). The Syntax and Semantics of the Proposed International Algebraic Language of the Zurich ACM-GAMM Conference. Proceedings of the International Conference on Information Processing, UNESCO, Paris, France."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Banzhaf, W., Poli, R., Schoenauer, M., and Fogarty, T.C. (1998). Grammatical evolution: Evolving programs for an arbitrary language. Genetic Programming, Springer. EuroGP 1998. Lecture Notes in Computer Science.","DOI":"10.1007\/BFb0055923"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Poli, R., Nordin, P., Langdon, W.B., and Fogarty, T.C. (1999). Evolving Multi-line Compilable C Programs. Genetic Programming, Springer. EuroGP 1999. Lecture Notes in Computer Science.","DOI":"10.1007\/3-540-48885-5"},{"key":"ref_59","unstructured":"Ryan, C., O\u2019Neill, M., and Collins, J.J. (1998, January 24\u201326). Grammatical evolution: Solving trigonometric identities. Proceedings of the Mendel 1998: 4th International Mendel Conference on Genetic Algorithms, Optimisation Problems, Fuzzy Logic, Neural Networks, Rough Sets, Brno, Czech Republic."},{"key":"ref_60","unstructured":"Puente, A.O., Alfonso, R.S., and Moreno, M.A. (2002, January 22\u201325). Automatic composition of music by means of grammatical evolution. Proceedings of the APL \u201902: Proceedings of the 2002 Conference on APL: Array Processing Languages: Lore, Problems, and Applications, Madrid, Spain."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.eswa.2016.03.012","article-title":"Optimization of neural networks through grammatical evolution and a genetic algorithm","volume":"56","author":"Roisenberg","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1162\/evco_a_00302","article-title":"Modular Grammatical Evolution for the Generation of Artificial Neural Networks","volume":"30","author":"Soltanian","year":"2022","journal-title":"Evol. Comput."},{"key":"ref_63","first-page":"23","article-title":"Constant creation in grammatical evolution","volume":"1","author":"Dempsey","year":"2007","journal-title":"Int. J. Innov. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Galv\u00e1n-L\u00f3pez, E., Swafford, J.M., O\u2019Neill, M., Brabazon, A., and PacMan, E.a.M. (2010). Controller Using Grammatical Evolution. Applications of Evolutionary Computation. EvoApplications 2010, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-642-12239-2_17"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Shaker, N., Nicolau, M., Yannakakis, G.N., Togelius, J., and O\u2019Neill, M. (2012, January 11\u201314). Evolving levels for Super Mario Bros using grammatical evolution. Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (CIG), Granada, Spain.","DOI":"10.1109\/CIG.2012.6374170"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1002\/ese3.568","article-title":"Particle swarm grammatical evolution for energy demand estimation","volume":"8","author":"Colmenar","year":"2020","journal-title":"Energy Sci. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TEVC.2013.2281527","article-title":"Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems","volume":"17","author":"Sabar","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ryan, C., Kshirsagar, M., Vaidya, G., Cunningham, A., and Sivaraman, R. (2022). Design of a cryptographically secure pseudo random number generator with grammatical evolution. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-11613-x"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2008.01.017","article-title":"Neural network construction and training using grammatical evolution","volume":"72","author":"Tsoulos","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"12210","DOI":"10.1016\/j.eswa.2009.04.065","article-title":"Location of amide I mode of vibration in computed data utilizing constructed neural networks","volume":"36","author":"Papamokos","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1016\/j.neucom.2008.12.004","article-title":"Solving differential equations with constructed neural networks","volume":"72","author":"Tsoulos","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Poli, R., and Langdon, W.B. (1998). Genetic Programming with One-Point Crossover, Springer.","DOI":"10.1007\/978-1-4471-0427-8_20"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.patrec.2008.02.007","article-title":"Selecting and constructing features using grammatical evolution","volume":"29","author":"Gavrilis","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","article-title":"Universal Approximation Using Radial-Basis-Function Networks","volume":"3","author":"Park","year":"1991","journal-title":"Neural Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5438","DOI":"10.1109\/TIE.2011.2164773","article-title":"Advantages of Radial Basis Function Networks for Dynamic System Design","volume":"58","author":"Yu","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_76","first-page":"161","article-title":"Creating classification rules using grammatical evolution","volume":"9","author":"Tsoulos","year":"2020","journal-title":"Int. J. Comput. Intell. Stud."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"100830","DOI":"10.1016\/j.softx.2021.100830","article-title":"GenClass: A parallel tool for data classification based on Grammatical Evolution","volume":"16","author":"Anastasopoulos","year":"2021","journal-title":"SoftwareX"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Ruggeri, F., Kenett, R.S., and Faltin, F.W. (2008). Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability, John Wiley & Sons, Inc.","DOI":"10.1002\/9780470061572"},{"key":"ref_80","unstructured":"Koski, T., and Noble, J. (2011). Bayesian Networks: An Introduction, John Wiley & Sons."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1109\/TNN.2009.2026902","article-title":"Privacy-Preserving Backpropagation Neural Network Learning","volume":"20","author":"Chen","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_83","first-page":"80","article-title":"Individual Comparisons by Ranking Methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Int. Biom. Soc."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:38:29Z","timestamp":1760031509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":83,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bdcc9060142"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9060142","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,22]]}}}