IEEE Computational Intelligence Magazine - August 2021 - 6

the constructed model and deduce it from
data at higher abstraction levels. Experimental
analyses conducted comprehensively
on both synthetic and real datasets
indicated the efficiency of our method in
terms of training time and predictive performance
in comparison to other types of
fuzzy min-max neural networks and
common machine learning algorithms. "
Discrete and Smoothed Resampling
Methods for Interval-Valued Fuzzy
Numbers, by M. Romaniuk and O.
Hryniewicz, IEEE Transactions on
Fuzzy Systems, Vol. 29, No. 3,
March 2021, pp. 599-611.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2957253
" In this article, we propose two new
resampling algorithms for the simulation
of bootstrap-like samples of interval-valued
fuzzy numbers (IVFNs). These
methods (namely, the d-method and the
s-method) reuse a primary sample (an
initial set) of IVFNs to generate a secondary
sample, which also consists of
this type of fuzzy numbers, and simultaneously
utilize existing dependencies in
pairs of some characteristic points of
IVFNs. During a corresponding resampling
step, a non-parametric approach is
used. Additionally, we apply a widely
used assumption about the Gaussian kernel
densities. The proposed methods in
some way resemble Efron's bootstrap,
but, contrary to this classical approach,
they generate " not exactly the same as
previous " IVFNs, so it leads to a greater
diversity of the obtained secondary sample.
We also numerically check the quality
of the introduced methods using a
few more statistically oriented approaches
together with four similarity measures
and three types of IVFNs. "
IEEE Transactions on Evolutionary
Computation
Realistic Constrained Multiobjective
Optimization Benchmark Problems
From Design, by C. Picard and
J. Schiffmann, IEEE Transactions on
Evolutionary Computation, Vol. 25, No. 2,
April 2021, pp. 234-246.
Digital Object Identifier: 10.1109/
TEVC.2020.3020046
" Multiobjective optimization is
increasingly used in engineering to design
new systems and to identify design tradeoffs.
Yet, design problems often have
objective functions and constraints that are
expensive and highly nonlinear. Combinations
of these features lead to poor convergence
and diversity loss with common
algorithms that have not been specifically
designed for constrained optimization.
Constrained benchmark problems exist,
but they do not necessarily represent the
challenges of engineering problems. In this
article, a framework to design electromechanical
actuators, called multiobjective
design of actuators (MODAct), is presented
and 20 constrained multiobjective optimization
test problems are derived from
the framework with a specific focus on
constraints. The full source code is made
available to ease its use. The effects of the
constraints are analyzed through their
impact on the Pareto front as well as on
the convergence performance. A constraint
landscape analysis approach is followed
and extended with three new
metrics to characterize the search and
objective spaces. The features of MODAct
are compared to existing test suites to
highlight the differences. In addition, a
convergence analysis using NSGA-II,
NSGA-III, and C-TAEA on MODAct
and existing test suites suggests that the
design problems are indeed difficult due to
the constraints. In particular, the number
of simultaneously violated constraints in
newly generated solutions seems key in
understanding the convergence challenges.
Thus, MODAct offers an efficient framework
to analyze and handle constraints in
future optimization algorithm design. "
IEEE Transactions on Games
A Multifaceted Surrogate Model for
Search-Based Procedural Content Generation,
by D. Karavolos, A. Liapis, and
G. Yannakakis, IEEE Transactions on
Games, Vol. 13, No. 1, March 2021,
pp. 11-22.
Digital Object Identifier: 10.1109/
TG.2019.2931044
6 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2021
" This paper proposes a framework
for the procedural generation of level and
ruleset components of games via a surrogate
model that assesses their quality and
complementarity. The surrogate model
combines level and ruleset elements as
input and gameplay outcomes as output,
thus constructing a mapping between
three different facets of games. Using this
model as a surrogate for expensive gameplay
simulations, a search-based generator
can adapt content toward a target gameplay
outcome. Using a shooter game as
the target domain, this paper explores
how parameters of the players' character
classes can be mapped to both the level's
representation and the gameplay outcomes
of balance and match duration.
The surrogate model is built on a deep
learning architecture, trained on a large
corpus of randomly generated sets of levels,
classes, and simulations from game
playing agents. Results show that a
search-based generative approach can
adapt character classes, levels, or both
toward designer-specified targets. The
model can thus act as a design assistant or
be integrated in a mixed-initiative tool.
Most importantly, the combination of
three game facets into the model allows it
to identify the synergies between levels,
rules, and gameplay and orchestrate the
generation of the former two toward
desired outcomes. "
IEEE Transactions on Cognitive
and Developmental Systems
Exoskeleton Online Learning and
Estimation of Human Walking
Intention Based on Dynamical
Movement Primitives, by S. Qiu, W.
Guo, D. Caldwell, and F. Chen, IEEE
Transactions on Cognitive and Developmental
Systems, Vol. 13, No. 1, March
2021, pp. 67-79.
Digital Object Identifier: 10.1109/
TCDS.2020.2968845
" Human walking intention estimation
is a critical step for the active assistance
control of lower limb exoskeleton, be -
cause the purpose of active assistance
control is human motion assistance rather
than human motion tracking. Complying
IEEE Computational Intelligence Magazine - August 2021

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