DEAP: evolutionary algorithms made easy
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Countries where authors are citing DEAP: evolutionary algorithms made easy
This map shows the geographic impact of DEAP: evolutionary algorithms made easy. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by DEAP: evolutionary algorithms made easy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DEAP: evolutionary algorithms made easy more than expected).
Fields of papers citing DEAP: evolutionary algorithms made easy
This network shows the impact of DEAP: evolutionary algorithms made easy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the DEAP: evolutionary algorithms made easy.
About DEAP: evolutionary algorithms made easy
This paper, published in 2012, received 1.1k indexed citations . Written by Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné covering the research area of Artificial Intelligence and Computational Theory and Mathematics. It is primarily cited by scholars working on Artificial Intelligence (351 citations), Electrical and Electronic Engineering (151 citations), Computational Theory and Mathematics (142 citations), Computer Vision and Pattern Recognition (103 citations) and Computer Networks and Communications (94 citations). Published in Journal of Machine Learning Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w52838935.