{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:45:20Z","timestamp":1761896720437,"version":"3.38.0"},"reference-count":41,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2016,12,18]],"date-time":"2016-12-18T00:00:00Z","timestamp":1482019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of High Performance Computing Applications"],"published-print":{"date-parts":[[2018,9]]},"abstract":"<jats:p> In this paper we present a new strategy for deploying massive runs of evolutionary algorithms with the well-known Evolutionary Computation Library (ECJ) tool, which we combine with the MapReduce model so as to allow the deployment of computing intensive runs of evolutionary algorithms on big data infrastructures. Moreover, by addressing a hard real life problem, we show how the new strategy allows us to address problems that cannot be solved with more traditional approaches. Thus, this paper shows that by using the Hadoop framework ECJ users can, by means of a new parameter, choose where the run will be launched, whether in a Hadoop based infrastructure or in a desktop computer. Moreover, together with the performed tests we address the well-known face recognition problem with a new purpose: to allow a genetic algorithm to decide which are the more relevant interest points within the human face. Massive runs have allowed us to reduce the set from about 60 to just 20 points. In this way, recognition tasks based on the solution provided by the genetic algorithm will work significantly quicker in the future, given that just 20 points will be required. Therefore, two goals have been achieved: (a) to allow ECJ users to launch massive runs of evolutionary algorithms on big data infrastructures and also (b) to demonstrate the capabilities of the tool to successfully improve results regarding the problem of face recognition. <\/jats:p>","DOI":"10.1177\/1094342016678302","type":"journal-article","created":{"date-parts":[[2017,12,31]],"date-time":"2017-12-31T15:33:09Z","timestamp":1514734389000},"page":"706-720","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deploying massive runs of evolutionary algorithms with ECJ and Hadoop: Reducing interest points required for face recognition"],"prefix":"10.1177","volume":"32","author":[{"given":"Francisco","family":"Ch\u00e1vez","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Extremadura, Spain"}]},{"given":"Francisco","family":"Fern\u00e1ndez de Vega","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Extremadura, Spain"}]},{"given":"Daniel","family":"Lanza","sequence":"additional","affiliation":[{"name":"CERN (European Organization for Nuclear Research)"}]},{"given":"C\u00e9sar","family":"Benavides","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma Metropolitana, Departamento de Ingener\u00eda El\u00e9ctrica, M\u00e9xico"}]},{"given":"Juan","family":"Villegas","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma Metropolitana, Departamento de Electr\u00f3nica, M\u00e9xico"}]},{"given":"Leonardo","family":"Trujillo","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico de Tijuana Calzada Del Tecnol\u00f3gico S\/N, M\u00e9xico"}]},{"given":"Gustavo","family":"Olague","sequence":"additional","affiliation":[{"name":"CICESE, M\u00e9xico"}]},{"given":"Graciela","family":"Rom\u00e1n","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma Metropolitana, Departamento de Ingener\u00eda El\u00e9ctrica, M\u00e9xico"}]}],"member":"179","published-online":{"date-parts":[[2016,12,18]]},"reference":[{"key":"bibr1-1094342016678302","first-page":"733","volume-title":"Proceedings MAEB 2015","author":"Benavides C","year":"2015"},{"key":"bibr2-1094342016678302","volume-title":"Efficient and accurate parallel genetic algorithms","volume":"1","author":"Cantu-Paz E","year":"2000"},{"key":"bibr3-1094342016678302","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31153-1_7"},{"key":"bibr4-1094342016678302","unstructured":"Cloudera (2015) Available at: http:\/\/www.cloudera.com."},{"key":"bibr5-1094342016678302","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"bibr6-1094342016678302","doi-asserted-by":"publisher","DOI":"10.1145\/1629175.1629198"},{"key":"bibr7-1094342016678302","doi-asserted-by":"publisher","DOI":"10.1504\/IJCAT.2013.052807"},{"key":"bibr8-1094342016678302","unstructured":"ECJ (2015) ECJ: A Java-based evolutionary computation research system. 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