{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:16:25Z","timestamp":1762787785961,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union: Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan","award":["TAEDK-06195"],"award-info":[{"award-number":["TAEDK-06195"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Earthquake predictability remains a central challenge in seismology. Are earthquakes inherently unpredictable phenomena, or can they be forecasted through advances in technology? Contemporary seismological research continues to pursue this scientific milestone, often referred to as the \u2018Holy Grail\u2019 of earthquake prediction. In the direction of earthquake prediction based on historical data, the Grammatical Evolution technique of GenClass demonstrated high predictive accuracy for earthquake magnitude. Similarly, our research team follows this line of reasoning, operating under the belief that nature provides a pattern that, with the appropriate tools, can be decoded. What is certain is that, over the past 30 years, scientists and researchers have made significant strides in the field of seismology, largely aided by the development and application of artificial intelligence techniques. Artificial Neural Networks (ANNs) were first applied in the domain of seismology in 1994. The introduction of deep neural networks (DNNs), characterized by architectures incorporating two hidden layers, followed in 2002. Subsequently, recurrent neural networks (RNNs) were implemented within seismological studies as early as 2007. Most recently, grammatical evolution (GE) has been introduced in seismological studies (2025). Despite continuous progress in the field, achieving the so-called \u201ctriple prediction\u201d\u2014the precise estimation of the time, location, and magnitude of an earthquake\u2014remains elusive. Nevertheless, machine learning and soft computing approaches have long played a significant role in seismological research. Concerning these approaches, significant advancements have been achieved, both in mapping seismic patterns and in predicting seismic characteristics on a smaller geographical scale. In this way, our research analyzes historical seismic events from 2004 to 2011 within the latitude range of 21\u00b0\u201379\u00b0 longitude range of 33\u00b0\u2013176\u00b0. The data is categorized and classified, with the aim of employing grammatical evolution techniques to achieve more accurate and timely predictions of earthquake magnitudes. This paper presents a systematic effort to enhance magnitude prediction accuracy using GE, contributing to the broader goal of reliable earthquake forecasting. Subsequently, this paper presents the superiority of GenClass, a key element of the grammatical evolution techniques, with an average error of 19%, indicating an overall accuracy of 81%.<\/jats:p>","DOI":"10.3390\/a18110710","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:07:31Z","timestamp":1762787251000},"page":"710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of Earthquakes Using Grammatical Evolution"],"prefix":"10.3390","volume":"18","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, 45110 Ioannina, 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, 45110 Ioannina, Greece"}]},{"given":"Vasileios","family":"Charilogis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2888-6515","authenticated-orcid":false,"given":"Chrysostomos","family":"Stylios","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"ref_1","first-page":"237","article-title":"International Handbook of Earthquake and Engineering Seismology: Part A","volume":"81","author":"Lee","year":"2002","journal-title":"Int. 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