BOA: the Bayesian optimization algorithm
Impact in
Classified as
- Journal
- Genetic and Evolutionary Computation Conference
In The Last Decade
doi.org/w7822905 →Countries where authors are citing BOA: the Bayesian optimization algorithm
This map shows the geographic impact of BOA: the Bayesian optimization algorithm. 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 BOA: the Bayesian optimization algorithm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites BOA: the Bayesian optimization algorithm more than expected).
Fields of papers citing BOA: the Bayesian optimization algorithm
This network shows the impact of BOA: the Bayesian optimization algorithm. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the BOA: the Bayesian optimization algorithm.
About BOA: the Bayesian optimization algorithm
This paper, published in 1999, received 627 indexed citations . Written by Martin Pelikán, David E. Goldberg and Erick Cantú‐Paz covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (442 citations), Computational Theory and Mathematics (181 citations), Computer Networks and Communications (54 citations), Industrial and Manufacturing Engineering (50 citations) and Electrical and Electronic Engineering (43 citations). Published in Genetic and Evolutionary Computation Conference.
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/w7822905.