IEEE Computational Intelligence Magazine - August 2022 - 12

given T-S fuzzy model could be varied,
this article, in contrast, focuses on how
the number of inputs to a given T-S
fuzzy model can be manipulated. The
proposed extension is capable of changing
the number of inputs or transforming
the nonlinearity between the fuzzy
rules and the input dimensions. These
new features considerably increase the
modeling power of the TP model transformation,
allowing for further complexity
reduction and more powerful
control optimization to be achieved.
This article provides two examples to
show how the proposed extension can
be used in a routine-like fashion. "
IEEE Transactions on Evolutionary
Computation
Adaptive Multifactorial Evolutionary
Optimization for Multitask Reinforcement
Learning, by A. D. Martinez, J. Del Ser,
E. Osaba, and F. Herrera, IEEE Transactions
on Evolutionary Computation,
Vol. 26, No. 2, Apr 2022, pp. 233-247.
Digital Object Identifier: 10.1109/
TEVC.2021.3083362
" Evolutionary computation has
largely exhibited its potential to complement
conventional learning algorithms
in a variety of machine learning tasks,
especially those related to unsupervised
(clustering) and supervised learning. It
has not been until lately when the computational
efficiency of evolutionary
solvers has been put in prospective for
training reinforcement learning models.
However, most studies framed so far
within this context have considered
environments and tasks conceived in
isolation, without any exchange of
knowledge among related tasks. In this
manuscript we present A-MFEA-RL, an
adaptive version of the well-known
MFEA algorithm whose search and
inheritance operators are tailored for
multitask reinforcement learning environments.
Specifically, our approach
includes crossover and inheritance
mechanisms for refining the exchange
of genetic material, which rely on the
multilayered structure of modern deeplearning-based
reinforcement learning
models. In order to assess the performance
of the proposed approach, we
design an extensive experimental setup
comprising multiple reinforcement
learning environments of varying levels
of complexity, over which the performance
of A-MFEA-RL is compared to
that furnished by alternative nonevolutionary
multitask reinforcement learning
approaches. As concluded from
the discussion of the obtained results,
A-MFEA-RL not only achieves competitive
success rates over the simultaneously
addressed tasks, but also fosters the
exchange of knowledge among tasks
that could be intuitively expected to
keep a degree of synergistic relationship. "
IEEE Transactions on Games
Training a Gaming Agent on Brainwaves,
by B. Francisco, M. Juan, N. Natalia,
V. José, R. Rodrigo, and S. J. Miguel,
IEEE Transactions on Games, Vol. 14,
No. 1, Mar 2022, pp. 85-92.
Digital Object Identifier: 10.1109/
TG.2020.3042900
" Error-related potentials (ErrPs) are a
particular type of event-related potential
elicited by a person attending a recognizable
error. These electroencephalographic
IEEE Computational Intelligence Magazine - August 2022

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