Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

Abstract

With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently taking a major part of the ongoing research, and expected to be the key player in today's technologies. This paper introduces and investigates the applications of machine learning in antenna design. It covers the major aspects of machine learning, including its basic concept, differentiation with artificial intelligence and deep learning, learning algorithms, its wide applications in various technologies, with a main focus on its usage in antenna design. The review also includes a comparison of the results using machine learning in antenna design, compared to the conventional design methods.

Full text

Machine Learning in Antenna Design: An Overview
on Machine Learning Concept and Algorithms
Hilal M. El Misilmani and Tarek Naous
Electrical & Computer Eng. Department
Beirut Arab University
Beirut, Lebanon
hilal.elmisilmani@ieee.org, tareknaous@ieee.org
Abstract—With the growth and wide variety of available data,
advanced processing, and affordable data storage, machine learn-
ing is witnessing great attention in finding optimized solutions in
various fields. Machine learning techniques are currently taking
a major part of the ongoing research, and expected to be the
key player in today’s technologies. This paper introduces and
investigates the applications of machine learning in antenna
design. It covers the major aspects of machine learning, including
its basic concept, differentiation with artificial intelligence and
deep learning, learning algorithms, its wide applications in
various technologies, with a main focus on its usage in antenna
design. The review also includes a comparison of the results
using machine learning in antenna design, compared to the
conventional design methods.
Index Terms—Machine learning, artificial intelligence, antenna
design, neural networks, electromagnetic simulations
I. INTRODUCTION
Artificial Intelligence (AI) is the art of enabling machines
to perform tasks that require human thinking abilities, such
as learning, decision making, and problem solving. In sim-
ple words, AI is implementing human thinking ability in
machines. With the latest advances in big data availability,
software engineering capability and affordable high computing
power, AI is becoming an essential part of today’s research.
It is expected to affect most of our daily activities, with fun-
damental changes in science and engineering, and enormous
impacts on society, with the ability to create, transform, or
optimize different aspects and applications of our daily lives.
To be able to implement and achieve artificial intelligence,
several capabilities need to be possessed by the computer or
machine. For instance, natural language processing is required
for successful communication in English and knowledge rep-
resentation is needed for storing information. To answer ques-
tions and drawing conclusions using the information stored,
automated reasoning is required, whereas, to derive patterns
in data and make predictions and adapt to new conditions,
machine learning is necessary. For object perception and
manipulation computer vision and robotics are needed. By
using intelligent algorithms and large amount of data sets,
iterative processing allows AI software to learn automatically
from patterns or features [1].
Artificial Intelligence and Machine Learning (ML) are often
used interchangeably, however, as will be discussed hereafter
Fig. 1: Relationship between Artificial Intelligence, Machine
Learning, and Deep Learning
and investigated in this paper, machine learning is a large
subset of artificial intelligence, as shown in Fig. 1. In fact,
machine learning can be considered as an approach to achieve
AI applications.
Machine Learning is briefly described as getting useful
information out of data, achieved by developing reliable pre-
diction algorithms. These algorithms can be very powerful in
optimization, but their success rely on the condition and size
of the data collected. Therefore, machine learning is frequently
associated with statistics and data analysis [2], [3].
As for Neural Networks, they are defined as a type of
machine learning algorithms that tries to imitate how the
human brain works. They consist of layers of interconnected
nodes. Each node produces a nonlinear function of its input.
Deep Neural Networks (DNNs) are neural networks that have
more than one hidden layer [4], usually refereed to as Deep
Learning. Both of these algorithms are considered as types of
machine learning algorithms.
This paper presents an overview on machine learning, with a
major focus on investigating its usage in antenna design appli-
cations. The concept of machine learning is studied along with
its different learning algorithms. Next, an extensive review
of several antenna designs and electromagnetic computational
methods is investigated. The methods used to design the
antennas using machine learning in each is presented, along
with the outcome of each algorithm used.
600978-1-7281-4484-9/19/$31.00 ©2019 IEEE

TABLE I: The Three Main Categories Of Learning
Learning Category Description
Supervised Learning A model is trained on a data set
Predictions are made on new inputs
Unsupervised Learning A pattern is derived from the data
after exploring it
Reinforcement Learning Model takes decisions and learns
from its actions
Fig. 2: Schematic of an artificial neural network
II. MAC HI NE LEARNING OV ERV IE W
Machine learning is based on algorithms that can learn from
data without relying on rules-based programming [5]. It can be
generally divided to three key categories: supervised learning,
unsupervised learning, and reinforcement learning [2], [6]–[8],
listed in Table I. Additional learning scenarios that are less
widely used include: Semi-supervised learning, Transductive
Inference, On-line learning and Active Learning [2]. Each
category has different learning algorithms that belong to it.
In supervised learning, what a correct outputs looks like is
already known. After training the learning algorithm on a given
data set, the algorithm generalizes to give accurate predictions
to all possible inputs [7]. Supervised learning algorithms
include but not limited to: linear regression, logistic regression,
artificial neural networks, and support vector machines.
– Linear Regression [9]: consists of fitting a continuous
linear function through the data from which the algorithm
can make predictions on new inputs.
– Logistic Regression [9]: used in classification tasks where
it predicts the probability that a certain input corresponds
to one of the already known classes.
– Artificial Neural Networks [9], [10]: in neural networks,
large interconnections of ”neurons”, which are simple
computing cells, are employed to achieve good perfor-
mance. When complex functions with many features are
found, neural networks offer an alternate way to perform
ML. Neural networks are made of multiple layers, the
input layer, the output layer, and hidden layers between
the input and output layers, as shown in Figure 2. One of
the many types of neural networks is the feed-forward
neural network where a weighted sum of connected-
neurons output are received by each neuron as an input.
– Support Vector Machines (SVMs) [10], [11]: another type
of supervised learning algorithm. In particular, it is used
in classification and deals with the more difficult case of
non-linearly separable patterns by using kernel methods.
– k Nearest Neighbors [9]: considered among the simplest
of all machine learning algorithms. After memorizing the
training set, the output of any new input is predicted by
the algorithm based on the outputs of its closest neighbors
in the training set.
In unsupervised learning, what the results should look like is
not known, the algorithm derives a structure from the data after
identifying similarities in the inputs [7]. Unsupervised learning
algorithms include but not limited to: K-Means Clustering and
Dimentionality Reduction Algorithms.
– K-Means Clustering [3]: k-unique clusters are automati-
cally formed by this algorithm. It is a type of unsuper-
vised learning where variables in the data are grouped
together based on relationships among them.
– Dimentionality Reduction Algorithms [9]: such as the
Principle Component Analysis Algorithm (PCA) where
the goal of this algorithm is minimizing the projection
error by reducing the reducing every feature’s distance to
a certain projection line.
In reinforcement learning, no labeled data set is received by
the machine. Instead, information is collected after interacting
with the environment through different actions. The machine is
rewarded after each action, hence its objective is maximizing
this expected average reward where the action would become
optimal. An example of a reinforcement learning model is the
Markov Decision Process (MDP). [2], [8]
Other machine learning algorithms exist with less widely
usage, such as: Decision Trees [12], Boosting [13], Naive
Bayes [9], Bayesian Regularization [14], Kriging [15], and
more [9].
When solving real world machine learning problems, the
data set is split into three parts [9]: a training set used to train
the algorithm, a cross-validation set used for model selection,
and a test set used for testing the performance of the prediction
algorithm, that is checking if it succeeds to generalize on
inputs not previously seen.
Machine learning techniques have been widely employed in
communication technology, such as antenna selection in wire-
less communications [16], [17], smart grid networks where
machine learning can detect malicious events before occuring
[18], wireless networks where ANNs can be used for predict-
ing the mobility patterns and content requests of wireless users
[6], speech recognition where SVMs with kernel functions
can be useful to improve its generalization performance [19],
context aware computing in IoT [20], and much more [21].
III. MACH IN E LEARNING IN ANT EN NA DESIGN
Several papers have investigated the applications of ma-
chine learning in antenna design. It is expected that machine
learning can provide accelerated antenna design process while
maintaining high accuracy levels, with a minimization of
error and time saving, along with a possible prediction of
the antenna behavior, a better computational efficiency and
reduced number of necessary simulations.
In general, in order to apply machine learning in antenna
design, the following steps can be done:
601

Fig. 3: Reflectarray unit cell [26]
1) By multiple simulations, the electromagnetic character-
istics of an antenna are found out.
2) These characteristics are stored in a database and used
as a data set for training a certain machine learning
algorithm.
3) The antenna that gives the closest results is designed by
the algorithm after making predictions, depending on the
needs of the designer.
In this section, a detailed review of several antenna de-
sign papers using machine learning is provided. For a clear
investigation, the papers discussed here are divided to two
main categories: machine learning for parameter optimization,
and machine learning for enhancing evolutionary computation
algorithms.
A. Machine Learning For Parameter Optimization
One approach of using machine learning in antenna design
is training a learning algorithm on data collected from previous
simulations to optimize the antenna parameters.
Based on Local-Periodicity (FW-LP), successful analysis
and optimization has already been achieved using a Full-Wave
analysis [22], [23], however, for space applications, faster
computations are required. Although this has been addressed
previously using Artificial Neural Networks (ANNs) [24],
[25], the results were limited. In [26], the design of shaped-
beam reflectarray unit cell using Support Vector Machine
(SVM) has been presented. Two sets of four parallel dipoles
are used in the design, shown in Fig. 3. To accelerate the reflec-
tarray antennas design machine learning algorithm is used. The
reflection coefficient matrix is first characterized using SVM.
The scattering parameters of the unit cell dimensions are then
derived. The influence of the discretization of the angle of
incidence is also taken into account in the simulations. The
results in paper using SVM are then compared to those in
[27] based on the Method of Moments (MoM-LP) in terms
of speed and accuracy. The results showed that the sequential
design using SVM can be accelerated by a factor of 880, and
the parallel design can also by accelerated by a factor of 566.
Fig. 4 showing the gain pattern comparison of the SVM and
the MoM-LP using the real angles of incidence.
(a)
(b)
Fig. 4: Gain pattern comparison of SVM and MoM (a)
elevation and (b) azimuth [26]
The design of a rectangular microstrip antenna using Sup-
port Vector Machines (SVMs) has been also presented in [28].
SVMs are used here for regression, and hence it may be called
Support Vector Regressors (SVR). The machine learning al-
gorithm is trained on a dataset that includes measured values
of the input impedance, operation bandwidth and resonant
frequency of the antenna. Artificial Neural Networks are also
employed at the same time to compare the results with those
obtained using SVR. It was shown that employing machine
learning algorithms in the design of microstrip antennas gives
better and more accurate characterization than the theoretical
results. Comparing SVR and ANN, it was shown that SVR
provides better computation efficiency since it has a faster
convergence rate, with less training time and test time needed
in comparison to ANN.
The design of a planar inverted-F antenna (PIFA), shown
in Fig. 5, with magneto dielectric Nano-composite (MDNC),
using the Bayesian Regularization algorithm as the neural
network learning process, has been presented in [14]. Starting
with the nano-magnetic material’s volume fraction and particle
radius, the different antenna parameters, such as radiation
efficiency, gain, resonant frequency, bandwidth and others, can
602

(a)
(b)
Fig. 5: PIFA antenna structure: (a) top view, (b) side view [29]
Fig. 6: Machine learning output vs target [29]
be calculated with high accuracy using machine learning. For
this design two databases are created. The first one contains
material properties (volume fraction and particle radius). The
second one contains electrical properties of the material,
such as electric and magnetic loss tangent, permittivity and
permeability. To create a relation between the performance
of the antenna and its properties, machine learning algorithm
is trained using the two databases as input and output. New
models for permeability and permittivity of the composite
material are also proposed in [14]. Using these equations with
only 42 samples created, a total error of only 7% is obtained
between machine learning data and the second database. Fig.
6 shows a comparison of the Machine Learning Output and
the target (all of the data stored in second database) regression
curve. It was concluded that machine learning techniques can
be very useful in minimization of error and acceleration of the
cycle time for new materials synthetization, and in expecting
the behavior of the antenna without performing extensive time
consuming simulations. Optimized results can be obtained by
increasing the number of data samples used with the algorithm.
Fig. 7: A comparison of the accuracy and time saving using
Neural Networks [15]
The design of reflectarrays using the Kriging algorithm
instead of the classical full-wave solvers has been presented in
[15]. Machine learning is used here to predict the response of
complex unit cells to design high-performance reflectarrays.
The scattering matrix of complex reflectarray elements is
predicted while reducing computational time. The model is
trained on a training set of N samples, where N ∈[500,20000],
to learn the input output relationship. A comparison of the
accuracy and time saving using the proposed method is shown
in Fig. 7. It was concluded that using the Kriging model, a 99%
time saving can be reached, while maintaining a prediction
error below 5%.
The design of a W-Band slotted waveguide antenna array,
shown in Fig. 8, by employing artificial neural networks
has been also presented in [30]. The input layer of the
neural network consists of seven design parameters which
are the lengths and orientation angles of the coupling slots
(lc, θ1, θ2, θ3, θ4, θ5), in addition to the length of the radiating
slots (lR). A data set of 189 examples are obtained by simu-
lations in HFSS and stored in a database which will be used
for training, cross-validation and testing of the model. After
obtaining the optimized values of the design parameters, the
antenna is fabricated using the SLA 3D printing techniques.
The simulated S11 characteristic as well as the gain of the
antenna are compared to those measured and a good agreement
with some slight errors has been shown.
B. Machine Learning For Enhancing Evolutionary Computa-
tion Algorithms
Another approach of using machine learning in antenna de-
sign is embedding it in Evolutionary Algorithms. Evolutionary
Algorithms (EAs) are taking important interest in computing
sciences. Using evolutionary mechanisms involved in natural
selection, evolutionary algorithms allow to find approximate
solutions to optimization problems. EAs have been used in
previous works [31]–[33] for the purpose of antenna design
and optimization. To improve their performance, machine
learning techniques have been used successfully in Evolution-
ary algorithms. When using EAs, databases of adequate size
603

(a)
(b)
Fig. 8: Slotted Waveguide Antenna (a) top view, (b) bottom
view. [30]
are produced which allows to use machine learning techniques
[34].
Particle Swarm Optimization Algorithm (PSOA), a type of
evolutionary algorithm, is used in [35] to design a multi-
band patch antenna using artificial neural networks. After the
geometrical parameters of the antenna are decided by the
PSOA, a mapping function is built by the ANN so that the
frequencies and associated bandwidths can be related to the
dimensional parameters of the antenna. The flow chart of the
optimizer used with four input parameters, is shown in Fig.
10. The results showed that the design process can be speed
up by eliminating the need for time-consuming simulations,
therefore reducing the computational burden significantly. The
design antenna has been also fabricated and tested, with good
analogy between the measured and simulated results in [35].
In [36], the design of an E-shaped antenna by combining
differential evolution (DE) with the Kriging algorithm, has
been presented. Six antenna parameters are optimized, the feed
position Px, the slot position Ps, the patch width W, the slot
width Ws, the patch length L and the slot length Ls, shown in
Fig. 11. It is suggested that similar results of other optimization
techniques can be obtained while reducing the number of
necessary simulations by 80%. The magnitude of the S11
parameter is desired to be minimized at frequencies 5.0GHz
and 5.5GHz. After the proposed algorithm was run 5 times,
optimum solutions were found, and good prediction accuracy
was exhibited by the model. The predicted and simulated S11
Fig. 9: Regression plot [35]
Fig. 10: Flow chart of proposed modified optimizer [35]
curves are also shown in Fig. 12. Combining machine learning
with evolutionary algorithms has been proven to provide a
faster convergence rate with a similar solution quality in
comparison to optimization methods, self-adaptive differential
evolution [37] and wind driven optimization [38]. It was shown
that the same optimization goals can be reached while reducing
the number of simulations needed by self-adaptive differential
evolution [37] and wind driven optimization [38] by 82.3%
and 77.9% respectively.
The design of an ultrawide band microstrip antenna by
604

TABLE II: Comparison of the different machine learning techniques used in the investigated papers
Paper Antenna Type Learning
Algorithm Used Compared To Results
[26] Reflectarrays SVM MoM & ANN Accelerated design process while
maintaining high accuracy levels
[14] Planar Inverted F-antenna (PIFA) Bayesian Regularization Minimization of error and acceleration of
cycle time for new materials synthetization
[15] Reflectarrays Kriging Time saving can reach 99.9% while
maintaining a prediction error below 5%
[29] Planar Inverted F-Antenna (PIFA) ANN Conventional Simulations Possible prediction of antenna behavior
without extensive electromagnetic simulations
[28] Rectangular Microstrip Antenna SVM ANN Better computation efficiency with
a faster convergence rate
[30] Slotted Waveguide Antenna ANN Conventional Simulations
Computation of several antenna parameters
with good agreement with the simulated and
fabricated results
[35] Antenna (SWA) ANN Conventional Simulations Design process can be sped up by eliminating
the need for time-consuming simulations
[36] Stacked Patch Antenna Kriging Conventional Simulations
Similar results of other optimization techniques
can be obtained while reducing the number of
necessary simulations by 80%
[39] E-Shaped Antenna Linear Regression Conventional Simulations The optimum results were found without any
necessary simulations
[40] Microstrip Antenna Gaussian Process ML Differential Evolution
The speed of the design and optimization
procedure by more than four times
compared with differential evolution
Fig. 11: E-shaped patch antenna [36]
combining regression with an evolutionary algorithm has been
presented in [39]. Machine learning technique is employed
here along with evolutionary algorithm in the estimation of
fitness function behaviors: the bandwidth (BW), the return loss
(RL) and the central frequency division (CFD). The regressive
machine learning algorithm used allows to fit a curve through
a discrete set of known data points which are the antenna
parameters obtained previously by simulations. A prototype
algorithm has been used to find the optimized values for
Wsand Ls, shown in Fig. 13, while keeping other antenna
parameters constant and considering design restrictions (BW
>9.0GHz, RL <-20dB and CFD <0.37Hz). It was shown
that, using 170 datasets, the optimum results were found
while meeting the required restrictions. The perception on the
behavior of the objectives (BW, RL and CFD) increases as a
Fig. 12: The simulated and predicted S11 curves of the optimal
design [36]
Fig. 13: Microstrip antenna with investigated parameters [39]
larger number of datasets is used.
The design of inter-chip antenna, a four-element linear array
antenna and a two-dimensional array have been presented in
605

[40]. A new method for designing antennas called surrogate
model assisted differential evolution for antenna synthesis
(SADEA) has been proposed. This method combines Gaussian
Process Machine Learning and a Differential Evolution (DE)
Algorithm. Using this method in the design of the three differ-
ent antennas, the results showed that, using machine learning,
SADEA enhanced the speed of the design and optimization
procedure by more than four times compared with DE. In
[41] Less time consumption has been also achieved in the
design of an isotropic wire antenna by combining a machine
learning technique with an evolutionary algorithm, the fitness
proportionate selection.
By employing Support Vector Machines (SVMs), the design
of a regular microstrip antenna has been presented in [42].
Better characterization with higher accuracy is achieved by
using SVMs. A comparison of the obtained results with the
results of a different approach that uses Artificial Neural
Networks (ANNs) is also presented.
A summary of the different papers using machine learning
in antenna design are summarized in Table II.
IV. CHALLENGES IN MACHINE LEARNING
Although Machine Learning is very useful, it comes with
a lot of challenges. The most common challenges include but
are not limited to:
1) The choice of learning algorithm: It is not easy to
decide what algorithm to choose as there are a great
number of them. This depends directly on what is being
predicted and also on the type of data acquired. A good
practice is to always visualize the data before choosing
the algorithm.
2) Problem Formulation: Beginning with wrong assump-
tions usually lead to worthless results that can cost a
lot of time. It is necessary to know what area of the
problem is best to spend time working on.
3) Getting enough data: Some data can be hard to find
or obtain. In antenna design, multiple simulations are
needed in order to obtain a training set.
4) Pre-processing of data: To ensure that the learning
algorithm performs adequately, multiple steps need to
be performed on the data, such as data cleaning, nor-
malization and feature selection, which would cost time
in case of very large datasets.
5) Debugging the algorithm: Knowing what to do next can
also be challenging. When problems, such as high bias
and high variance occur, it is crucial to know what
steps to take. This requires following some diagnosis
techniques such as plotting the learning curves.
V. CONCLUSION
This paper presented an overview on machine learning with
an investigation of its concepts, differentiation with artificial
intelligence and deep learning, its different learning algo-
rithms and techniques. An extensive investigation is presented
on the usage of machine learning in antenna design, for
which its advantages with respect to the traditional design
and computational techniques have been also studied. It was
seen that machine learning can provide accelerated antenna
design process while maintaining high accuracy levels, with a
minimization of error and time saving, along with a possible
prediction of the antenna behavior, a better computational
efficiency and reduced number of necessary simulations.
REFERENCES
[1] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach,
3rd Edition. Pearson Education, 2016.
[2] M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine
Learning. The MIT Press, 2012.
[3] P, Harrington, Machine Learning in Action. Manning Publications, 2012.
[4] R. Jiang, X. Wang, S. Cao, J. Zhao and X. Li, “Deep Neural Networks
for Channel Estimation in Underwater Acoustic OFDM Systems,” IEEE
Access, vol. 7, pp. 23579–23594, 2019.
[5] D. Pyle and C. S. Jose, “An executive’s guide to machine learning,”
tech. rep., McKinsey & Company, June 2015.
[6] M. Chen, U. Challita, W. Saad , C. Yin and M. Debbah, “Machine
learning for wireless networks with artificial intelligence: A tutorial on
neural networks,” CoRR, vol. abs/1710.02913, 2017.
[7] S. Marsland, Machine Learning: An Algorithmic Perspective, Second
Edition. Chapman and Hall/CRC, 2014.
[8] A. Burkov, The Hundred-Page Machine Learning Book. Andriy Burkov,
2019.
[9] S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning:
From Theory to Algorithms. Cambridge University Press, 2014.
[10] S. O. Haykin, Neural Networks and Learning Machines. Prentice Hall,
2008.
[11] K. Crammer and Y. Singer, “On the algorithmic implementation of
multiclass kernel-based vector machines,” Journal of Machine Learning
Research, vol. 2, p. 265–292, 2001.
[12] L. Hyafil and R. L. Rivest, “Constructing optimal binary decision trees
is NP-complete,” Information Processing Letters, vol. 5, no. 1, p. 15–17,
1976.
[13] Y. Freund and R. Schapire, “A decision-theoretic generalization of on-
line learning and an application to boosting,” European Conference on
Computational Learning Theory (EuroCOLT), p. 23–37, 1995.
[14] C. Gianfagna, M. Swaminathan, P. M. Raj, R. Tummala and G. An-
tonini, “Enabling antenna design with nano-magnetic materials using
machine learning,” 2015 IEEE Nanotechnology Materials and Devices
Conference (NMDC), pp. 1–5, Anchorage, AK, 2015.
[15] L. Tenuti, G. Oliveri, D. Bresciani and A. Massa, “Advanced learning-
based approaches for reflectarrays design,” 2017 11th European Confer-
ence on Antennas and Propagation (EUCAP), pp. 84–87, Paris, 2017.
[16] X. Wang, P. Wang and X. Wang, “Adaptive Sparse Array Reconfigura-
tion based on Machine Learning Algorithms,” 2018 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP),
pp. 1159–1163, Calgary, AB, 2018.
[17] J. Joung, “Machine Learning-Based Antenna Selection in Wireless Com-
munications,” IEEE Communications Letters, vol. 20, no. 11, pp. 2241–
2244, Nov. 2016.
[18] Z. M. Fadlullah and M. M. Fouda and N. Kato and X. Shen and Y.
Nozaki, “An early warning system against malicious activities for smart
grid communications,” IEEE Network, vol. 25, no. 5, pp. 50–55, 2011.
[19] B. A. Sonkamble and D. D. Doye, “Use of Support Vector Machines
through Linear-Polynomial (LP) Kernel for Speech Recognition,” Inter-
national Conference on Advances in Mobile Network, Communication
and Its Applications, pp. 46–49, 2012.
[20] C. Perera and A. Zaslavsky and P. Christen and D. Georgakopoulos,
“Context Aware Computing for The Internet of Things: A Survey,” IEEE
Communications Surveys Tutorials, vol. 16, no. 1, pp. 414–454, 2014.
[21] S. S. W. Samek and T. Wiegand, “The convergence of machine learning
and communications,” CoRR, vol. abs/1708.08299, 2017.
[22] J. Huang and J. Antonio Encinar, Reflectarray Antennas. Wiley-IEEE
Press, 2007.
[23] D. R. Prado, M. Arrebola, M. R. Pino, R. Florencio, R. R. Boix, J. A.
Encinar and F. Las-Heras, “Efficient Crosspolar Optimization of Shaped-
Beam Dual-Polarized Reflectarrays Using Full- Wave Analysis for the
Antenna Element Characterization,” IEEE Transactions on Antennas and
Propagation, vol. 65, no. 2, pp. 623–635, 2017.
606

[24] P. Robustillo, J. Zapata, J. A. Encinar and J. Rubio, “ANN Charac-
terization of Multi-Layer Reflectarray Elements for Contoured-Beam
Space Antennas in the Ku-Band,” IEEE Transactions on Antennas and
Propagation, vol. 60, no. 7, pp. 3205–3214, 2012.
[25] V. Richard R., Loison, R. Gillard, H. Legay and M. Romier, “Loss
analysis of a reflectarray cell using ANNs with accurate magnitude pre-
diction,” 2017 11th European Conference on Antennas and Propagation
(EUCAP), pp. 2396–2399, 2017.
[26] D. R. Prado, J. A. L´
opez-Fern´
andez, M. Arrebola and G. Goussetis,
“Efficient Shaped-Beam Reflectarray Design Using Machine Learning
Techniques,” 2018 15th European Radar Conference (EuRAD), pp. 525–
528, Madrid, 2018.
[27] R. Florencio, R. R. Boix and J. A. Encinar, “Enhanced MoM Analysis
of the Scattering by Periodic Strip Gratings in Multilayered Substrates,”
IEEE Transactions on Antennas and Propagation, vol. 61, no. 10,
pp. 5088–5099, 2013.
[28] N. T. Tokan and F. Gune, “Support Vector Characterization of the
Microstrip Antennas Based on Measurements,” Progress In Electromag-
netics Research B, vol. 5, pp. 49–61, 2008.
[29] C. Gianfagna, H. Yu, M. Swaminathan, R. Pulugurtha, R. Tum-
mala, and G. Antonini, “Machine-Learning Approach for Design
of Nanomagnetic-Based Antennas,” Journal of Electronic Materials,
vol. 46, no. 8, pp. 4963–4975, 2017.
[30] J. Tak, A. Kantemur, Y. Sharma and H. Xin, “A 3-D-PrintedW-Band
Slotted Waveguide Array Antenna Optimized Using Machine Learning,”
IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 11,
pp. 2008–2012, Nov. 2018.
[31] A. Hoorfar, “Evolutionary Programming in Electromagnetic Optimiza-
tion: A Review,” IEEE Transactions on Antennas and Propagation,
vol. 55, no. 3, pp. 523–537, March 2007.
[32] J. M. Johnson and Y. Rahmat-Samii, “Genetic algorithms and method
of moments (GA/MOM) for the design of integrated antennas,” IEEE
Transactions on Antennas and Propagation, vol. 47, no. 10, pp. 1606–
1614, Oct. 1999.
[33] M. D. Gregory, Z. Bayraktar and D. H. Werner, “Fast Optimization
of Electromagnetic Design Problems Using the Covariance Matrix
Adaptation Evolutionary Strategy,” IEEE Transactions on Antennas and
Propagation, vol. 59, no. 4, pp. 1275–1285, April 2011.
[34] J. Zhang, Z. Zhan, Y. Lin, N. Chen, Y. Gong, J. Zhong, H. S. H. Chung,
Y. Li and Y. Shi, “Evolutionary Computation Meets Machine Learning:
A Survey,” IEEE Computational Intelligence Magazine, vol. 6, no. 4,
pp. 68–75, Nov. 2011.
[35] S. K. Jain, “Bandwidth Enhancement Of Patch Antennas Using Neu-
ral Network Dependent Modified Optimizer,” International Journal of
Microwave and Wireless Technologies, vol. 8, no. 7, pp. 1111–1119,
2016.
[36] X. H. Chen, X. X. Guo, J. M. Pei and W. Y. Man, “A hybrid algorithm of
differential evolution and machine learning for electromagnetic structure
optimization,” 2017 32nd Youth Academic Annual Conference of Chinese
Association of Automation (YAC), pp. 755–759, Hefei, 2017.
[37] M. D. Gregory and Z. Bayraktar and D. H. Werner, “Fast Optimization
of Electromagnetic Design Problems Using the Covariance Matrix
Adaptation Evolutionary Strategy,” IEEE Transactions on Antennas and
Propagation, vol. 59, no. 4, pp. 1275–1285, 2011.
[38] Z. Bayraktar and M. Komurcu and J. A. Bossard and D. H. Werner,
“The Wind Driven Optimization Technique and its Application in
Electromagnetics,” IEEE Transactions on Antennas and Propagation,
vol. 61, no. 5, pp. 2745–2757, 2013.
[39] C. R. Silva and S. R. Martins, “An Adaptive Evolutionary Algorithm
for UWB Microstrip Antennas Optimization Using a Machine Learning
Technique.,” Microw. Opt. Technol. Lett., vol. 55, no. 8, pp. 1864–1868,
2013.
[40] B. Liu, H. Aliakbarian, Z. Ma, G. A. E. Vandenbosch, G. Gielen and P.
Excell, “An Efficient Method for Antenna Design Optimization Based
on Evolutionary Computation and Machine Learning Techniques,” IEEE
Transactions on Antennas and Propagation, vol. 62, no. 1, pp. 7–18, Jan.
2014.
[41] Saifullah and B. Ahmed, “Machine learning for isotropic antenna
design,” 2018 22nd International Microwave and Radar Conference
(MIKON), pp. 683–687, Poznan, 2018.
[42] N. T. Tokan and F. Gunes, “Support vector design of the microstrip
antenna,” 2008 IEEE 16th Signal Processing, Communication and
Applications Conference, pp. 1–4, Aydin, 2008.
607