IEEE Computational Intelligence Magazine - February 2021 - 19

Digital Object Identifier: 10.1109/
TFUZZ.2019.2922152
" In recent years, the demand for
developing low computational cost
methods to deal with uncertainties in
forecasting has been increased. Probabilistic forecasting is a class of forecasting
in which the method provides intervals
or probability distributions as outcomes
of its forecasting. The aim of this paper
is, therefore, proposing a new forecasting approach based on fuzzy time series
(FTS) that takes advantage of fuzzy and
stochastic patterns on data and is capable to deal with point, interval, and distribution forecasts. The method pro----posed
was empirically tested with typical
financial time series, and the results
were compared with other standard
FTS and statistical methods. The re--
sults show that the proposed method
obtained accurate results and outperformed standard FTS methods. The
proposed method also combines versatility, scalability, and low computational
cost, making it useful on a wide range
of application scenarios. "
Patch Learning, by D. Wu and J. M.
Mendel, IEEE Transactions on Fuzzy
Systems, Vol. 28, No. 9, September
2020, pp. 1996-2008.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2930022
" There have been different strategies to improve the performance of a
ma--chine learning model, e.g., increasing
the depth, width, and/or nonlinearity of
the model, and using ensemble learning
to aggregate multiple base/weak learners in parallel or in series. This article
proposes a novel strategy called patch
learning (PL) for this problem. It consists of three steps: first, train an initial
global model using all training data; second, identify from the initial global
model the patches that contribute the
most to the learning error, and train a
(local) patch model for each such patch;
and, third, update the global model
using training data that do not fall into
any patch. To use a PL model, we first
determine if the input falls into any
patch. If yes, then the corresponding

patch model is used to compute the
output. Otherwise, the global model is
used. We explain in detail how PL can
be implemented using fuzzy systems.
Five regression problems on onedimensional (1-D)/2-D/3-D curve fitting, nonlinear system identification,
and chaotic time-series prediction, verified its effectiveness. To our knowledge, the PL idea has not appeared in
the literature before, and it opens up a
promising new line of research in ma--
chine learning. "
IEEE Transactions on
Evolutionary Computation

A Multifactorial Evolutionary Algorithm
for Multitasking Under Interval Uncertainties, by J. Yi, J. Bai, H. He, W.
Zhou, and L. Yao, IEEE Transactions
on Evolutionary Computation, Vol. 24,
No. 5, October 2020, pp. 908-922.
Digital Object Identifier: 10.1109/
TEVC.2020.2975381
" Various real-world applications with
interval uncertainty, such as the path
planning of mobile robot, layout of
radio frequency identification readers
and solar desalination, can be formulated
as an interval multiobjective optimization problem (IMOOP), which is usually transformed into one or a series of
certain problems to solve by using evolutionary algorithms. However, a definite characteristic among them is that
only a single optimization task can be
catched up at a time. Inspired by the
multifactorial evolutionary algorithm
(MFEA), a novel interval MFEA
(IMFEA) is proposed to solve IMOOPs
simultaneously using a single population
of evolving individuals. In the proposed
method, the potential interdependency
across related problems can be explored
in the unified genotype space, and multitasks of multiobjective interval optimization problems are solved at once by
promoting knowledge transfer for the
greater synergistic search to improve the
convergence speed and the quality of
the optimal solution set. Specifically, an
interval crowding distance based on
shape evaluation is calculated to evaluate

the interval solutions more comprehensively. In addition, an interval dominance
relationship based on the evolutionary
state of the population is designed to
obtain the interval confidence level,
which considers the difference of average convergence levels and the relative
size of the potential possibility between
individuals. Correspondingly, the strict
transitivity proof of the presented
dominance relationship is given. The
efficacy of the associated evolutionary
algorithm is validated on a series of
benchmark test functions, as well as a
real-world case of robot path planning
with many terrains that provides insight
into the performance of the method in
the face of IMOOPs. "
IEEE Transactions on Games

Adaptive Music Composition for Games,
by P. E. Hutchings and J. McCormack, IEEE Transactions on Games,
Vol. 12, No. 3, September 2020, pp.
270-280.
Digital Object Identifier: 10.1109/
TG.2019.2921979
" The generation of music that
adapts dynamically to content and
actions has an important role in building more immersive, memorable, and
emotive game experiences. To date, the
development of adaptive music systems
(AMSs) for video games is limited both
by the nature of algorithms used for
real-time music generation and the
limited modeling of player action,
game-world context, and emotion in
current games. We propose that these
issues must be addressed in tandem for
the quality and flexibility of adaptive
game music to significantly improve.
Cognitive models of knowledge organization and emotional effect are integrated with multimodal, multiagent
composition techniques to produce a
novel AMS. The system is integrated
into two stylistically distinct games.
Gamers reported an overall higher
immersion and correlation of music
with game-world concepts with the
AMS than that with the original game
soundtracks in both the games. "

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IEEE Computational Intelligence Magazine - February 2021

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