Bob McKay

2.9k total citations
92 papers, 1.5k citations indexed

About

Bob McKay is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Bob McKay has authored 92 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Artificial Intelligence, 17 papers in Molecular Biology and 8 papers in Computational Theory and Mathematics. Recurrent topics in Bob McKay's work include Evolutionary Algorithms and Applications (46 papers), Metaheuristic Optimization Algorithms Research (40 papers) and Reinforcement Learning in Robotics (11 papers). Bob McKay is often cited by papers focused on Evolutionary Algorithms and Applications (46 papers), Metaheuristic Optimization Algorithms Research (40 papers) and Reinforcement Learning in Robotics (11 papers). Bob McKay collaborates with scholars based in South Korea, Australia and Vietnam. Bob McKay's co-authors include Nguyễn Xuân Hoài, Michael O’Neill, Hussein A. Abbass, Daryl Essam, Nguyen Quang Uy, Peter A. Whigham, Edgar Galván, Бо Лю, David J. Paull and Dongkyun Kim and has published in prestigious journals such as Aquaculture, Information Sciences and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Bob McKay

86 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bob McKay South Korea	20	1.1k	280	236	154	137	92	1.5k
Nicholas Freitag McPhee United States	16	1.4k 1.3×	389 1.4×	257 1.1×	111 0.7×	81 0.6×	41	1.8k
Carlos J. Alonso Spain	12	911 0.9×	229 0.8×	114 0.5×	178 1.2×	369 2.7×	47	1.9k
Frank D. Francone Germany	6	767 0.7×	226 0.8×	133 0.6×	75 0.5×	57 0.4×	10	1.1k
Rafael Stubs Parpinelli Brazil	13	811 0.8×	104 0.4×	263 1.1×	226 1.5×	192 1.4×	62	1.4k
Paul E. Utgoff United States	21	1.5k 1.4×	111 0.4×	289 1.2×	539 3.5×	289 2.1×	49	2.3k
Hitoshi Iba Japan	24	889 0.8×	426 1.5×	171 0.7×	31 0.2×	117 0.9×	121	1.4k
Sreerama K. Murthy United States	6	690 0.7×	82 0.3×	201 0.9×	382 2.5×	138 1.0×	9	1.2k
Fethi Jarray Tunisia	7	312 0.3×	151 0.5×	69 0.3×	78 0.5×	95 0.7×	24	937
Zhihai Wang China	12	787 0.7×	83 0.3×	86 0.4×	294 1.9×	159 1.2×	47	1.1k
Yun Niu China	13	371 0.4×	291 1.0×	65 0.3×	123 0.8×	64 0.5×	26	866

Countries citing papers authored by Bob McKay

Since Specialization

Citations

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

Fields of papers citing papers by Bob McKay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bob McKay

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

All Works

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20 of 20 papers shown

Qin, Zhenyue, Yang Liu, Pan Ji, et al.. (2022). Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition. IEEE Transactions on Neural Networks and Learning Systems. 35(4). 4783–4797. 65 indexed citations

Qin, Zhenyue, Tom Gedeon, & Bob McKay. (2018). Why don't the modules dominate?. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 121–122. 3 indexed citations

McKay, Bob, et al.. (2013). Collective Dynamics and Homeostatic Emergence in Complex Adaptive Ecosystem. 332–339.

Jeong, Kwang‐Seuk, et al.. (2012). Machine Learning for Predictive Management: Short and Long term Prediction of Phytoplankton Biomass using Genetic Algorithm Based Recurrent Neural Networks. International Journal of Environmental Research. 6(1). 95–108. 19 indexed citations

McKay, Bob, et al.. (2012). Evolutionary Dynamics and Ecosystems Feedback in Two Dimensional Daisyworld. 91–98. 2 indexed citations

McKay, Bob, et al.. (2012). Evolutionary Dynamics and Ecosystems Feedback in Two Dimensional Daisyworld. 91–98. 3 indexed citations

McKay, Bob, et al.. (2011). Hexagonal Tortoise Problem Solving using Constraint Programming. Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong. 38(1). 27–40.

Kim, Dongkyun, Kwang‐Seuk Jeong, Bob McKay, et al.. (2010). Model development in freshwater ecology with a case study using evolutionary computation. Journal of Ecology and Environment. 33(4). 275–288. 3 indexed citations

McKay, Bob, et al.. (2009). Cognitive Based Context Aware Reference History Management Tool. International Conference on Human-Computer Interaction. 227–231. 1 indexed citations

10.

Mori, Naoki, et al.. (2009). A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification. Journal of Advanced Computational Intelligence and Intelligent Informatics. 13(3). 237–244. 5 indexed citations

11.

Liu, Bo, et al.. (2009). Entropy-based metrics in swarm clustering. International Journal of Intelligent Systems. 24(9). 989–1011. 5 indexed citations

12.

Mori, Naoki, et al.. (2008). A New Method for Simplifying Algebraic Expressions in Genetic Programming called Equivalent Decision Simplification. 2008. 1671–1676. 1 indexed citations

13.

McKay, Bob, et al.. (2008). Incremental Clustering Based on Swarm Intelligence. 1 indexed citations

14.

McKay, Bob, et al.. (2007). Using compression to understand the distribution of building blocks in genetic programming populations. 2501–2508. 7 indexed citations

15.

McKay, Bob, et al.. (2004). Grammar model-based program evolution. 478–485. 61 indexed citations

16.

McKay, Bob & John Slaney. (2002). AI 2002 : Advances in Artificial Intelligence : 15th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, December 2-6, 2002 : proceedings. Springer eBooks. 1 indexed citations

17.

McKay, Bob. (2000). Fitness sharing in genetic programming. Genetic and Evolutionary Computation Conference. 435–442. 49 indexed citations

18.

McKay, Bob. (1999). Simulated evolution and learning : Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL '98, Canberra, Australia, November 24-27, 1998 : selected papers. Springer eBooks. 1 indexed citations

19.

McKay, Bob, R. J. Parker, & W. Guenter. (1986). RESPONSE TO SELECTION FOR BODY WEIGHT OR FEED EFFICIENCY IN MICE TESTED ON CORN, RYE AND WHEAT DIETS. Canadian Journal of Animal Science. 66(2). 389–397. 1 indexed citations

20.

McKay, Bob. (1980). Dietary fibre dilution as a means for controlling broiler breeder growth. Mspace (University of Manitoba). 3 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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