Martin A. Keane

3.0k total citations
47 papers, 1.4k citations indexed

About

Martin A. Keane is a scholar working on Artificial Intelligence, Molecular Biology and Electrical and Electronic Engineering. According to data from OpenAlex, Martin A. Keane has authored 47 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 22 papers in Molecular Biology and 13 papers in Electrical and Electronic Engineering. Recurrent topics in Martin A. Keane's work include Evolutionary Algorithms and Applications (44 papers), Metaheuristic Optimization Algorithms Research (32 papers) and VLSI and FPGA Design Techniques (13 papers). Martin A. Keane is often cited by papers focused on Evolutionary Algorithms and Applications (44 papers), Metaheuristic Optimization Algorithms Research (32 papers) and VLSI and FPGA Design Techniques (13 papers). Martin A. Keane collaborates with scholars based in United States, United Kingdom and Japan. Martin A. Keane's co-authors include Forrest H Bennett, John R. Koza, David André, J.R. Koza, André Duarte, M. J. V. Streeter, Jessen Yu, William Mydlowec, J. R. Koza and James P. Rice and has published in prestigious journals such as The Journal of the Acoustical Society of America, Computer Methods in Applied Mechanics and Engineering and Information Sciences.

In The Last Decade

Martin A. Keane

44 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Martin A. Keane United States 16 1.0k 338 302 231 143 47 1.4k
Forrest H Bennett United States 13 864 0.9× 250 0.7× 271 0.9× 208 0.9× 114 0.8× 26 1.2k
J.R. Koza United States 14 608 0.6× 147 0.4× 223 0.7× 125 0.5× 91 0.6× 27 870
Hitoshi Iba Japan 24 889 0.9× 426 1.3× 94 0.3× 171 0.7× 112 0.8× 121 1.4k
Nasimul Noman Japan 17 873 0.9× 445 1.3× 465 1.5× 460 2.0× 162 1.1× 63 1.9k
Kumara Sastry United States 25 1.2k 1.2× 147 0.4× 98 0.3× 525 2.3× 117 0.8× 67 1.7k
Hitoshi Iba Japan 21 1.5k 1.5× 578 1.7× 533 1.8× 678 2.9× 292 2.0× 81 2.6k
Thomas B�ck Netherlands 3 547 0.5× 91 0.3× 204 0.7× 189 0.8× 158 1.1× 3 1.0k
Jason Lohn United States 17 471 0.5× 104 0.3× 288 1.0× 198 0.9× 47 0.3× 68 1.0k
David André United States 12 798 0.8× 177 0.5× 94 0.3× 187 0.8× 65 0.5× 30 1.0k
Marco Tomassini Switzerland 16 741 0.7× 184 0.5× 65 0.2× 300 1.3× 32 0.2× 52 1.0k

Countries citing papers authored by Martin A. Keane

Since Specialization
Citations

This map shows the geographic impact of Martin A. Keane'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 Martin A. Keane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin A. Keane more than expected).

Fields of papers citing papers by Martin A. Keane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin A. Keane. 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 Martin A. Keane. The network helps show where Martin A. Keane may publish in the future.

Co-authorship network of co-authors of Martin A. Keane

This figure shows the co-authorship network connecting the top 25 collaborators of Martin A. Keane. A scholar is included among the top collaborators of Martin A. Keane 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 Martin A. Keane. Martin A. Keane is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Koza, John R., M. J. V. Streeter, & Martin A. Keane. (2009). Routine Human-Competitive Machine Intelligence by Means of Genetic Programming. 15 indexed citations
2.
Streeter, M. J. V., et al.. (2008). Routine high-return human-competitive automated problem-solving by means of genetic programming. Information Sciences. 178(23). 4434–4452. 17 indexed citations
3.
Koza, John R., Martin A. Keane, James P. Rice, & John H. Holland. (2004). USE OF GENETIC PROGRAMMING TO FIND AN IMPULSE RESPONSE FUNCTION IN SYMBOLIC FORM(. World Journal of Stem Cells. 11(9). 693–704.
4.
Koza, John R., et al.. (2004). Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source. 1 indexed citations
5.
Koza, John R., Martin A. Keane, M. J. V. Streeter, Thomas P. Adams, & Lee W. Jones. (2004). Invention and creativity in automated design by means of genetic programming. Artificial intelligence for engineering design analysis and manufacturing. 18(3). 245–269. 13 indexed citations
6.
Koza, John R., Martin A. Keane, & M. J. V. Streeter. (2003). Evolving Inventions. Scientific American. 288(2). 52–59. 38 indexed citations
7.
8.
Koza, J.R., Martin A. Keane, & M. J. V. Streeter. (2003). What's AI done for me lately? Genetic programming's human-competitive results. IEEE Intelligent Systems. 18(3). 25–31. 33 indexed citations
9.
Streeter, M. J. V., Martin A. Keane, & John R. Koza. (2002). Iterative refinement of computational circuits using genetic programming. Genetic and Evolutionary Computation Conference. 877–884. 7 indexed citations
10.
Koza, J.R., Martin A. Keane, & James P. Rice. (2002). Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification. IEEE International Conference on Neural Networks. 191–198. 16 indexed citations
11.
Koza, J.R., Jessen Yu, Martin A. Keane, & William Mydlowec. (2002). Use of conditional developmental operators and free variables in automatically synthesizing generalized circuits using genetic programming. 1391. 5–15. 3 indexed citations
12.
Koza, John R., et al.. (2000). REVERSE ENGINEERING OF METABOLIC PATHWAYS FROM OBSERVED DATA USING GENETIC PROGRAMMING. PubMed. 434–445. 73 indexed citations
13.
Koza, John R., Martin A. Keane, Jessen Yu, Forrest H Bennett, & William Mydlowec. (2000). Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming. Genetic Programming and Evolvable Machines. 1(1-2). 121–164. 88 indexed citations
14.
Koza, John R., Jessen Yu, Martin A. Keane, & William Mydlowec. (2000). Automatic synthesis of electrical circuits containing a free variable using genetic programming. 477–484. 5 indexed citations
15.
Bennett, Forrest H, et al.. (1999). Evolution by means of genetic programming of analog circuits that perform digital functions. 1477–1483. 12 indexed citations
16.
Koza, J. R., Forrest H Bennett, André Duarte, & Martin A. Keane. (1999). Genetic Programming: Turing's Third Way to Achieve Machine Intelligence. 1 indexed citations
17.
18.
Koza, John R., et al.. (1998). Evolving Sorting Networks using Genetic Programming an Reconfigurable Xilinx 6216 Field-Programmable Gate Array. 2 indexed citations
19.
Keane, Martin A., John R. Koza, & James P. Rice. (1993). Finding an Impulse Response Function Using Genetic Programming. 12 indexed citations
20.
Cornillon, Peter & Martin A. Keane. (1977). Simple model for simulating traffic noise spectra. The Journal of the Acoustical Society of America. 61(3). 739–743. 5 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.

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