IEEE Computational Intelligence Magazine - November 2022 - 14

are obtained with the IT-2 FSMs being
resilient to the high levels ofuncertainty
noise. We also prove that our IT-2
FSMs can overcome the drawbacks of
some existing similarity measures based
on the accuracy rate measure. In addition,
the proposed IT-2 FSMs arejoined
with fuzzy C-means algorithm as a clustering
method and the proposed system
is compared against the existing clustering
algorithms (type-1 fuzzy k-means,
type-1, and type-2 fuzzy C-means, cluster
forest, bagged clustering, evidence
accumulation, and random projection).
Relying on the clustering quality parameters
Rand C (equivalent to the standard
classification accuracy), the advanced
IT-2 FSMs show higher classification
accuracy of about 86% which outperforms
nearly the other classifiers. "
Incremental Feature Selection Using a
Conditional Entropy Based on Fuzzy
Dominance Neighborhood Rough Sets,
by B. Sang, H. Chen, L. Yang, T.
Li, and W. Xu, IEEE Transactions on
Fuzzy Systems, Vol. 30, No. 6, June
2022, pp. 1683-1697.
Digital Object Identifier: 10.1109/
TFUZZ.2021.3064686
" Incremental feature selection
approaches can improve the efficiency
of feature selection used for dynamic
datasets, which has attracted increasing
research attention. Nevertheless, there is
currently no work on incremental feature
selection approaches for dynamic
ordered data. Moreover, the monotonic
classification effect ofordered data is easily
affected by noise, so a robust feature
evaluation metric is needed for feature
selection algorithm. Motivated by these
two issues, we investigate incremental
feature selection approaches using a new
conditional entropy with robustness for
dynamic ordered data in this study. First,
we propose a new rough set model, i.e.,
fuzzy dominance neighborhood rough
sets (FDNRS). Second, a conditional
entropy with robustness is defined based
on FDNRS model, which is used as
evaluation metric for features and combined
with a heuristic feature selection
algorithm. Finally, two incremental feature
selection algorithms are designed on
the basis ofthe above researches. Experiments
are performed on ten public
datasets to evaluate the robustness of
the proposed metric and the performance
of the incremental algorithms.
Experimental results verify that the proposed
metric is robust and our incremental
algorithms are effective and
efficient for updating reducts in dynamic
ordered data. "
IEEE Transactions on Evolutionary
Computation
PSO-X: A Component-Based Frameworkfor
the Automatic Design ofParticle
Swarm Optimization Algorithms,by C.
L. Camacho-Villalon, M. Dorigo,
and T. St€utzle, IEEE Transactions on
Evolutionary Computation, Vol. 26,
No. 3, June 2022, pp. 402-416.
Digital Object Identifier: 10.1109/
TEVC.2021.3102863
" The particle swarm optimization
(PSO) algorithm has been the object of
many studies and modifications for more
than 25 years. Ranging from small
refinements to the incorporation of
sophisticated novel ideas, the majority of
modifications proposed to this algorithm
have been the result ofa manual process
in which developers try new designs
based on their own knowledge and
expertise. However, manually introducing
changes is very time consuming and
makes the systematic exploration of all
the possible algorithm configurations a
difficult process. In this article, we propose
to use automatic design to overcome
the limitations of having to
manually find performing PSO algorithms.
We develop a flexible software
framework for PSO, called PSO-X,
which is specifically designed to integrate
the use ofautomatic configuration
tools into the process ofgenerating PSO
algorithms. Our framework embodies a
large number of algorithm components
developed over more than 25 years of
research that have allowed PSO to deal
with a large variety of problems, and
14 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2022
uses irace, a state-of-the-art configuration
tool, to automatize the task of
selecting and configuring PSO algorithms
starting from these components.
We show that irace is capable offinding
high-performing instances ofPSO algorithms
never proposed before. "
IEEE Transactions on Games
Interactive Constrained MAP-Elites:
Analysis and Evaluation of the Expressiveness
of the Feature Dimensions by
A. Alvarez, S. Dahlskog, J. Font, and
J. Togelius, IEEE Transactions on
Games, Vol. 14, No. 2, June 2022,
pp. 202-211.
Digital Object Identifier: 10.1109/
TG.2020.3046133
" In this article, we propose the interactive
constrained multidimensional
archive of phenotypic elites (MAPElites),
a quality-diversity solution for
game content generation, implemented
as a new feature ofthe evolutionary dungeon
designer (EDD): a mixed-initiative
co-creativity tool for designing dungeons.
The feature uses the MAP-Elites
algorithm, an illumination algorithm that
segregates the population among several
cells depending on their scores with
respect to different behavioral dimensions.
Users can flexibly and dynamically
alternate between these dimensions anytime,
thus guiding the evolutionary process
in an intuitive way, and then
incorporate suggestions produced by the
algorithm in their room designs. At the
same time, any modifications performed
by the human user will feed back into
MAP-Elites, closing a circular workflow
ofconstant mutual inspiration. This article
presents the algorithm followed by an
in-depth evaluation of the expressive
range ofall possible dimension combinations
in several scenarios and discusses
their influence in the fitness landscape
and in the overall performance of the
procedural content generation in the
EDD. "
IEEE Computational Intelligence Magazine - November 2022

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