IEEE Systems, Man and Cybernetics Magazine - April 2021 - 21

were truly an inspiration. I was
Based on this, there are lots of
privileged to have worked with
opportunities to utilize the UFO
The UFO and its
and learned from him. May his
and its extensions in cybernetics.
extensions can be
soul rest in peace.
For example, the UFO, ULR, and
employed to realize
UNR can be applied to unknown
About the Author
data sets to automatically create
many engineering
Ali R. Al-Roomi (ali.ridha@dal
objective functions for optimizaproblems without
.ca) earned his B.Sc. degree in protion algorithms. They can be used
cess instrumentation and control
to translate real-world engineering
neglecting lessengineering and his M.Sc. degree
problems into mathematical modimportant variables
(Hons.) in electrical and electronels, which can then be harnessed
ics engineering from the Universiin ordinary and adaptive control
and parameters.
ty of Bahrain, Sakheer, Bahrain, in
systems through the Laplace
2006 and 2014, respectively. He
transform. This is very useful for
earned his Ph.D. degree in electricybernetics, where control syscal and computer engineering from Dalhousie University,
tems occupy one major part. In addition, because the
Halifax, Nova Scotia, Canada, in 2020. He is with the
UFO has been used to predict future values [2], it can be
Department of Electrical and Computer Engineering, Dalemployed online to forecast disturbances that might
housie University, Halifax, Nova Scotia, B3H 4R2, Canada.
occur in a system due to dynamic changes in operating
His research interests include machine learning, metavalues (voltage, current, impedance, frequency, and so
heuristic optimization algorithms, hybrid computing
on), surrounding weather conditions (temperature,
systems, power operation, power protection, state estimahumidity, atmospheric pressure, wind velocity, wind
tion, smart grid, power system design and analysis, sysdirection, light intensity, and so forth), aging and efficientem realization, load forecasting, and interdisciplinary
cy degrading, and similar applications. Finally, the UFO
engineering fields. He is a Member of IEEE.
and its extensions can be employed to realize many engineering problems without neglecting less-important variReferences
ables and parameters or/and simplifying the final models
based on assumptions. This is a great add-on tool for
[1] D. P. Filev, Q. Zhao, and J. Brine, " Cybernetics: Where shall we go? " in Proc. IEEE
cybernetics to govern things correctly and efficiently.
Int. Conf. Cybernetics (CYBCO), 2013, pp. 25-31.
[2] A. R. Al-Roomi and M. E. El-Hawary, " Universal functions originator, " Appl. Soft

Conclusion
It can be concluded that the cybernetic field is highly
immersed in ML. Modern computing systems are highly
accurate, but they lack explainability, which limits their
applications and exposes them to many legal issues. SR
techniques could be a good choice to compromise
between accuracy and explainability performance criteria, but their mechanisms are inflexible and require lots
of programming effort. Also, SRs cannot perform dimension reduction, dimension expansion, feature selection,
and high-dimensional function visualization. All these
technical issues could be addressed by the latest ML
technique, the UFO, and its extensions. Hybridizing the
UFO with other ML tools could solve some of these
weaknesses because the UFO can take care of the data
set nonlinearity issue before it is sent to the ML tools
known in the literature, such as LR, NLR, ANNs, and
SVMs. The main hybridization scheme is performed by
letting the UFO act as a UTU. It can also be hybridized in
many other ways.

Comput., vol. 94, p. 106,417, Sept. 2020. doi: 10.1016/j.asoc.2020.106417.
[3] C. Rudin, " Stop explaining black box machine learning models for high stakes
decisions and use interpretable models instead, " Nature Mach. Intell., vol. 1, no. 5, pp.
206-215, May 2019. doi: 10.1038/s42256-019-0048-x.
[4] R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, " Explainable machine learning
for scientific insights and discoveries, " IEEE Access, vol. 8, pp. 42,200-42,216, 2020.
doi: 10.1109/ACCESS.2020.2976199.
[5] N. Q. Uy, N. X. Hoai, M. O'Neill, R. I. McKay, and E. Galván-López, " Semantically-based
crossover in genetic programming: Application to real-valued symbolic regression, " Genetic
Program. Evolvable Mach., vol. 12, no. 2, pp. 91-119, June 2011. doi: 10.1007/s10710-0109121-2.
[6] Y. Wang, N. Wagner, and J. M. Rondinelli, " Symbolic regression in materials science, " MRS Commun., vol. 9, no. 3, pp. 793-805, 2019. doi: 10.1557/mrc.2019.85.
[7] J. R. Koza, " Genetic programming as a means for programming computers by
natural selection, " Statist. Comput., vol. 4, no. 2, pp. 87-112, June 1994. doi: 10.1007/
BF00175355.
[8] S. Forrest, " Genetic algorithms: Principles of natural selection applied
to computation, " Science, vol. 261, no. 5123, pp. 872-878, 1993. doi: 10.1126/
science.8346439.
[9] D. A. Novikov, Cybernetics: From Past to Future. Cham, Switzerland: Springer-Verlag,
2016.

Acknowledgment
I am indebted to my late supervisor Prof. Mohamed E.
El-Hawary whose vast knowledge, deep understanding,
wisdom, and virtues in the practice of his profession

	

[10] M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied Linear Statistical Models (Operations and Decision Sciences). Boston: McGraw-Hill/Irwin,
2005.



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IEEE Systems, Man and Cybernetics Magazine - April 2021

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