Evolutionary Intelligence

892 papers and 9.1k indexed citations i.

About

The 892 papers published in Evolutionary Intelligence in the last decades have received a total of 9.1k indexed citations. Papers published in Evolutionary Intelligence usually cover Artificial Intelligence (453 papers), Computer Vision and Pattern Recognition (180 papers) and Electrical and Electronic Engineering (127 papers) specifically the topics of Metaheuristic Optimization Algorithms Research (227 papers), Evolutionary Algorithms and Applications (156 papers) and Advanced Multi-Objective Optimization Algorithms (108 papers). The most active scholars publishing in Evolutionary Intelligence are Francisco Herrera, Raghavendra V. Kulkarni, D. R. Sarvamangala, Xin‐She Yang, Peter Dürr, Dario Floreano, Claudio Mattiussi, A. E. Eiben, Evert Haasdijk and Stefano Nolfi.

In The Last Decade

Fields of papers published in Evolutionary Intelligence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Evolutionary Intelligence. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Evolutionary Intelligence.

Countries where authors publish in Evolutionary Intelligence

Since Specialization
Citations

This map shows the geographic impact of research published in Evolutionary Intelligence. 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 papers published in Evolutionary Intelligence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evolutionary Intelligence more than expected).

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.

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