Mark Johnston

5.7k total citations
151 papers, 3.0k citations indexed

About

Mark Johnston is a scholar working on Artificial Intelligence, Computer Networks and Communications and Industrial and Manufacturing Engineering. According to data from OpenAlex, Mark Johnston has authored 151 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Artificial Intelligence, 37 papers in Computer Networks and Communications and 31 papers in Industrial and Manufacturing Engineering. Recurrent topics in Mark Johnston's work include Metaheuristic Optimization Algorithms Research (59 papers), Evolutionary Algorithms and Applications (44 papers) and Advanced Multi-Objective Optimization Algorithms (25 papers). Mark Johnston is often cited by papers focused on Metaheuristic Optimization Algorithms Research (59 papers), Evolutionary Algorithms and Applications (44 papers) and Advanced Multi-Objective Optimization Algorithms (25 papers). Mark Johnston collaborates with scholars based in United States, New Zealand and United Kingdom. Mark Johnston's co-authors include Mengjie Zhang, Steven Minton, Andrew B. Philips, Philip Laird, Su Nguyen, Kay Chen Tan, Urvesh Bhowan, Jun Zhang, Xin Yao and H. M. Adorf and has published in prestigious journals such as Nature, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Mark Johnston

142 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Johnston United States 26 1.4k 877 846 411 386 151 3.0k
Simon Lin Taiwan 7 1.1k 0.7× 1.6k 1.8× 456 0.5× 414 1.0× 204 0.5× 22 2.6k
Marco Locatelli Italy 29 675 0.5× 357 0.4× 324 0.4× 1.0k 2.5× 183 0.5× 155 2.6k
Michael A. Bender United States 35 854 0.6× 235 0.3× 2.0k 2.4× 367 0.9× 93 0.2× 183 3.9k
Jeff Schneider United States 33 2.0k 1.4× 65 0.1× 383 0.5× 283 0.7× 409 1.1× 157 3.9k
Peter Cheeseman United States 16 1.6k 1.1× 134 0.2× 693 0.8× 335 0.8× 243 0.6× 40 3.8k
Shang‐Hua Teng United States 37 1.2k 0.8× 207 0.2× 1.3k 1.5× 1.4k 3.5× 481 1.2× 154 4.9k
Heinz Mühlenbein Germany 25 2.6k 1.8× 305 0.3× 332 0.4× 1.1k 2.8× 183 0.5× 62 3.9k
Kai Cai Japan 23 431 0.3× 180 0.2× 1.0k 1.2× 733 1.8× 194 0.5× 137 2.5k
Neal E. Young United States 20 358 0.2× 168 0.2× 957 1.1× 412 1.0× 178 0.5× 65 1.9k
Anthony Skjellum United States 25 650 0.5× 109 0.1× 2.2k 2.6× 148 0.4× 80 0.2× 165 3.9k

Countries citing papers authored by Mark Johnston

Since Specialization
Citations

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

Fields of papers citing papers by Mark Johnston

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Johnston. 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 Mark Johnston. The network helps show where Mark Johnston may publish in the future.

Co-authorship network of co-authors of Mark Johnston

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Johnston. A scholar is included among the top collaborators of Mark Johnston 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 Mark Johnston. Mark Johnston 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.
Johnston, Mark, et al.. (2023). Pre-launch lessons learned from NASA’S deep space network support for the artemis I mission to the moon. Acta Astronautica. 210. 589–595. 3 indexed citations
2.
Al-Sahaf, Harith, Mengjie Zhang, Mark Johnston, & Brijesh Verma. (2015). Image descriptor: A genetic programming approach to multiclass texture classification. 2460–2467. 35 indexed citations
3.
Nguyen, Su, Mengjie Zhang, Mark Johnston, & Kay Chen Tan. (2013). Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments. Evolutionary Computation. 22(1). 105–138. 25 indexed citations
4.
Fu, Wenlong, Mark Johnston, & Jun Zhang. (2012). Genetic programming for edge detection using blocks to extract features. 855–862. 3 indexed citations
5.
Zhang, Jun, et al.. (2012). A spatial random-meaningful neighbourhood topology in pso for edge detection in noisy images. 1403–1404. 1 indexed citations
6.
Fu, Wenlong, Mark Johnston, & Mengjie Zhang. (2012). Genetic programming for edge detection based on figure of merit. 1483–1484. 2 indexed citations
7.
Johnston, Mark & Mark Giuliano. (2009). MUSE: The Multi-User Scheduling Environment for Multi-Objective Scheduling of Space Science Missions. ESA Special Publication. 673. 9. 7 indexed citations
8.
Giuliano, Mark & Mark Johnston. (2008). Multi-objective evolutionary algorithms for scheduling the James Webb Space Telescope. International Conference on Automated Planning and Scheduling. 107–115. 15 indexed citations
9.
Clement, Bradley J. & Mark Johnston. (2005). The deep space network scheduling problem. Innovative Applications of Artificial Intelligence. 1514–1520. 25 indexed citations
10.
Johnston, Mark & G. E. Miller. (2002). Artificial intelligence scheduling for NASA's Hubble Space Telescope. 52. 33–39.
11.
Johnston, Mark. (2002). Reconciling high-speed scheduling with dispatching in wafer fabs. 9. 107–110. 3 indexed citations
12.
Johnston, Mark. (1996). Scheduling Tools for Astronomical Observations. ASPC. 87. 62. 1 indexed citations
13.
Johnston, Mark. (1992). Spike: AI scheduling for Hubble Space Telescope after 18 months of orbital operations. NASA Technical Reports Server (NASA). 3 indexed citations
14.
Minton, Steven, Mark Johnston, Andrew B. Philips, & Philip Laird. (1992). Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence. 58(1-3). 161–205. 466 indexed citations
15.
Minton, Steven, Mark Johnston, Andrew B. Philips, & Philip Laird. (1990). Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method. National Conference on Artificial Intelligence. 181(2). 17–24. 190 indexed citations
16.
Johnston, Mark. (1988). Automated Observation Scheduling for the VLT. NASA STI Repository (National Aeronautics and Space Administration). 30. 1273. 10 indexed citations
17.
Miller, G. E., et al.. (1986). An expert system for ground support of the Hubble space telescope. 5 indexed citations
18.
Schwartz, D. A., R. E. Griffiths, J. Schwarz, R. Doxsey, & Mark Johnston. (1979). X-ray emitting BL Lacertae objects located by the scanning modulation collimator experiment on HEAO 1. The Astrophysical Journal. 229. L53–L53. 22 indexed citations
19.
Bradt, H., R. Doxsey, Mark Johnston, et al.. (1979). 3C 273 - X-ray variability, spectrum, and position. The Astrophysical Journal. 230. L5–L5. 4 indexed citations
20.
Griffiths, R. E., et al.. (1979). The optical identification of 2A 0311-227 with a new AM Herculis-type object. The Astrophysical Journal. 232. L27–L27. 11 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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