Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An evolutionary algorithm that constructs recurrent neural networks
1994658 citationsPeter J. Angeline, Gregory M. Saunders et al.profile →
Automatic design and manufacture of robotic lifeforms
Countries citing papers authored by Jordan Pollack
Since
Specialization
Citations
This map shows the geographic impact of Jordan Pollack's research. 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 Jordan Pollack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jordan Pollack more than expected).
This network shows the impact of papers produced by Jordan Pollack. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jordan Pollack. The network helps show where Jordan Pollack may publish in the future.
Co-authorship network of co-authors of Jordan Pollack
This figure shows the co-authorship network connecting the top 25 collaborators of Jordan Pollack.
A scholar is included among the top collaborators of Jordan Pollack based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jordan Pollack. Jordan Pollack is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pollack, Jordan, et al.. (2005). On the Coevolutionary Construction of Learnable Gradients.. National Conference on Artificial Intelligence. 35–40.1 indexed citations
3.
Pollack, Jordan. (2005). Nannon: A Nano Backgammon for Machine Learning Research..1 indexed citations
4.
Pollack, Jordan, et al.. (2005). Towards Metrics and Visualizations Sensitive to Coevolutionary Failures.. National Conference on Artificial Intelligence. 1–8.7 indexed citations
5.
Pollack, Jordan, et al.. (2005). Theme Preservation and the Evolution of Representation.. Indian International Conference on Artificial Intelligence. 1444–1463.1 indexed citations
6.
Pollack, Jordan, Mark A. Bedau, Phil Husbands, Takashi Ikegami, & Richard A. Watson. (2004). Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems. ePrints Soton (University of Southampton).42 indexed citations
7.
Nicolelis, Miguel A. L., Cherie R. Kagan, Usama M. Fayyad, et al.. (2001). The technology review ten. Technology Review. 104(1). 97–113.5 indexed citations
8.
Hornby, Gregory S. & Jordan Pollack. (2001). Body-brain co-evolution using L-systems as a generative encoding. Genetic and Evolutionary Computation Conference. 868–875.88 indexed citations
9.
Pollack, Jordan, et al.. (2000). Infinite RAAM: A Principled Connectionist Basis for Grammatical Competence. eScholarship (California Digital Library). 22(22).4 indexed citations
10.
Pollack, Jordan & Elizabeth Sklar. (2000). Cel: a framework for enabling an internet learning community. Educational Technology & Society. 3.12 indexed citations
11.
Watson, Richard A. & Jordan Pollack. (2000). Recombination without respect: schema combination and disruption in genetic algorithm crossover. Genetic and Evolutionary Computation Conference. 112–119.14 indexed citations
12.
Watson, Richard A. & Jordan Pollack. (1999). Incremental commitment in genetic algorithms. Genetic and Evolutionary Computation Conference. 710–717.15 indexed citations
13.
Saunders, Gregory M., John F. Kolen, Peter J. Angeline, & Jordan Pollack. (1997). Additive Modular Learning in Preemptrons. eScholarship (California Digital Library). 306(5). 960–971.1 indexed citations
14.
Pollack, Jordan & Alan Blair. (1996). Why did TD-Gammon Work?. Neural Information Processing Systems. 9. 10–16.12 indexed citations
15.
Maes, Pattie, Maja J. Matarić, Jean-Arcady Meyer, Jordan Pollack, & Stewart W. Wilson. (1996). Robotic “Food” Chains: Externalization of State and Program for Minimal-Agent Foraging. 625–634.44 indexed citations
16.
Saunders, Gregory M., Peter J. Angeline, & Jordan Pollack. (1993). Structural and Behavioral Evolution of Recurrent Networks. Neural Information Processing Systems. 6. 88–95.5 indexed citations
17.
Pollack, Jordan. (1990). Language Induction by Phase Transition in Dynamical Recognizers. Neural Information Processing Systems. 3. 619–626.1 indexed citations
18.
Kolen, John F. & Jordan Pollack. (1990). Back Propagation is Sensitive to Initial Conditions. Complex Systems. 3. 860–867.184 indexed citations
19.
Pollack, Jordan. (1988). Implications of Recursive Distributed Representations. Neural Information Processing Systems. 1. 527–536.28 indexed citations
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.