IEEE Computational Intelligence Magazine - February 2023 - 8

academic publications, Video Systems
implemented various neural network
architectures to meet the needs of its
customers.
Thanks to the advent of GPUs and
the emergence of a number of open
source projects in this field, the development
path has been 'easier', but challenges
and unknowns accompany every
step we take on this journey. Today,
the company integrates robotics,
machine vision, IoT and computational
intelligence to create solutions for the
manufacturing world, always with the
goal of helping the industry reduce
waste and energy needs in particular on
fields like steel production market,
automotive and hollow glass production
markets.
Peculiarity of Video Systems is the
ability to develop both the hardware
components (electronic boards,
mechanics, optics, embedded systems)
and the software components of a
machine vision solution integrating AI
based solutions.
Video Systems has been collaborating
internationally for several years
in research projects focused on the
Zero Defect Manufacturing paradigm
and today applies its knowledge in
the field of artificial intelligence to
create increasingly high-performance
and innovative solutions. The company
designs and produces quality
control systems based on embedded
platforms of its own creation combined
with AI solutions, and does so
with the perseverance that a major
objective such as sustainability challenges
requires. Alessandro firmly
believes in this vision, motivating his
team and the people around him
with statements such as " Ibelieve
that we can all contribute for a better
world, we need to use technology to
improve this world from the perspective
of sustainability, inclusiveness and
leave it to our children better than
we found it, " and in this path he is
convinced that associations such as
the IEEE are one of the ideal places
where people can contribute to current
sustainability challenges and on
new fields such as the new space race
that will allow us to develop innovative
technologies for a better world.
Outstanding Early Career Award
Bing Xue,
Victoria University of Wellington,
New Zealand
For contributions
to the development
and application of
evolutionary
machine learning.
Bing Xue received
the Ph.D.
degree in artificial
intelligence from the Victoria University
ofWellington (VUW), Wellington, New
Zealand, in 2014. She is currently a Professor
of artificial intelligence, and the
Deputy Head ofthe School for Engineering
and Computer Science, VUW. Her
research interests include evolutionary
computation, machine learning, Big Data,
feature selection/learning, evolving neural
networks, explainable AI, and their
real-world applications. She has more
than 300 papers published in fully refereed
international journals and conferences,
including many highly cited papers
and top most popular papers, and more
than 100 publications in prestigiousjournals,
such as IEEE TEVC, IEEE
TNNLS, IEEE TCYB, and Pattern Recognition.
She established and is currently
leading the Feature Analysis, Selection,
and Learning in Image and Pattern Recognition
(FASLIP) Group with VUW
with more than 25 staffand research students.
She was the recipient ofa number
of awards, including Best Paper Awards
from international conferences, and
Early Career Award, Research Excellence
Award and Supervisor Award from
her University. She is the Chair of the
Evolutionary Computation Technical
Committee, Editor of IEEE CIS Newsletter,
and a Member of several (sub-)
committees in IEEE CIS. She has also
been the Associate/Guest Editor or Editorial
Board Member for more than ten
international journals, including IEEE
TEVC and IEEE CIM. She has been
invited to give many plenary talks, keynotes,
tutorials or invited talks in international
conferences or events, including a
8 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2023
plenary talk in IEEE WCCI 2022. She is
also the Key Chair ofmany international
conferences, including the Conference
Chair ofCEC in IEEEWCCI 2024.
Outstanding PhD Dissertation
Award
Danial Yazdani,
for his thesis
entitled Particle
Swarm Optimization
for Dynamically
Changing
Environments
with Particular
Focus on Scalability
and Switching Cost for the Doctor of
Philosophy degree from Liverpool John
Moores University, Liverpool, U.K., in
October 2018.
Supervisors: Trung Thanh Nguyen,
Liverpool John Moores University, Liverpool,
U.K., andJuergen Branke, University
ofWarwick, Coventry, U.K..
Abstract: Change is an inescapable
aspect of natural and artificial systems,
and adaptation is central to their resilience.
Optimization problems are no
exception to this maxim. Indeed, the
search space of many problems changes
over time. Such optimization problems
are denoted as dynamic optimization
problems (DOPs) in the literature. This
thesis focuses on two important and
complex classes ofDOPs, namely robust
optimization over time (ROOT) and
large-scale DOPs.
The field of ROOT deals with
DOPs in which frequent changes ofthe
deployed solution are undesirable. This
can be due to the high cost ofswitching
the deployed solutions, the limitation of
the needed resources to deploy such
new solutions, and/or the system being
intolerant towards frequent changes of
the deployed solution. In this thesis, a
ROOT framework is proposed that
searches for robust solutions based on
the learned problem space characteristics.
This thesis then adapts the proposed
ROOT framework for tackling two
different classes ofROOT problems: 1)
the ones in which it is preferred to keep
the deployed solution as long as it
remains acceptable after environmental
IEEE Computational Intelligence Magazine - February 2023

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