(2002). Swarm Intelligence: From Natural to Artificial Systems. Connection Science: Vol. 14, No. 2, pp. 163-164.
Connection Science Template
Write in a clean editor, then format for Connection Science in one click — DocuGuru applies the official Taylor & Francis template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Connection Science format
Connection Science is a peer-reviewed journal published by Taylor & Francis, covering Neural Networks and Applications, Neural dynamics and brain function, Language and cultural evolution.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Connection Science 12 (3): 45–58.
Formats any DOI in Connection Science style. No sign-up. |
| Publishes research in | Neural Networks and Applications Neural dynamics and brain function Language and cultural evolution Topic Modeling Evolutionary Algorithms and Applications |
| ISSN | 0954-0091 |
| Citation impact (2-yr) | 4.69 |
| h-index | 75 |
| i10-index | 537 |
| Total citations | 28,583 |
| Article processing charge | $1,270 |
| Open access | Yes |
| Top institutions publishing here | Providence University |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Connection Science per year
Citation impact of Connection Science by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Connection Science
This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential solution. We replicate some of the experiments described by…
Abstract. Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic…
Using an ensemble of classifiers, instead of a single classifier, can lead to improved generalization. The gains obtained by combining however, are often affected more by the selection of what is presented to the combiner, than by the actual combining method that is chosen. In this paper we focus on data selection and classifier training…
This paper reviews research on combining artificial neural nets, and provides an overview of, and an introduction to, the papers contained in this special issue, and its companion (Connection Science, 9, 1). Two main approaches, ensemble-based, and modular, are identified and considered. An ensemble, or committee, is made up of a set of nets, each…