Computational Intelligence - February 2017 - 14

Pythagorean fuzzy sets (a generalization
of intuitionistic fuzzy sets). They first
characterize two lattices that have been
suggested for Pythagorean fuzzy sets and
then extend these results to the unit disc
of the complex plane. They thereby
identify two new complete, distributive
lattices over the unit disc, and explore
interpretations of them based on fuzzy
antonyms and negations."
Fault Detection and Isolation for Affine
Fuzzy Systems with Sensor Faults, by
H. Wang, G.-H. Yang, and D. Ye,
IEEE Transactions on Fuzzy Systems,
Vol. 24, No. 5, October 2016, pp.
1058-1071.
Digital Object Identifier: 10.1109/
TFUZZ.2015.2501414
"This paper investigates the fault
detection and isolation (FDI) problem
for a class of nonlinear systems with sensor outage faults. The considered nonlinear systems are described as affine
fuzzy models, and the system outputs are
chosen as the premise variables of fuzzy
models. Different from the existing
results, the influence of sensor faults on
premise variables is considered. As a
result, the well-known parallel distributed compensation scheme cannot be
used for FDI filters design. By using the
structural information encoded in the
fuzzy rules, the affine fuzzy system is
represented by multiple operatingregime-based models in fault-free case
and faulty cases. In the multiple-model
scheme, a bank of piecewise FDI filters
are constructed, each of them is based
on the affine fuzzy model that describes
the system in the presence of a specified
fault. The fault-dependent residual signals generated from the filters are used
for detecting and isolating the specified
fault. The FDI filter design conditions
are obtained in the formulation of linear
matrix inequalities."
IEEE Transactions on
Evolutionary Computation

A Primary Theoretical Study on Decomposition-Based Multiobjective Evolutionary Algorithms, by Y.-L. Li, Y.-R.

Zhou, Z.-H. Zhan, and J. Zhang,
IEEE Transactions on Evolutionary
Computation, Vol. 20, No. 4, August
2016, pp. 563-576.
Digital Object Identifier: 10.1109/
TEVC.2015.2501315
"Decomposition-based multiobjective evolutionary algorithms (MOEAs)
have been studied a lot and have been
widely and successfully used in practice.
However, there are no related theoretical studies on this kind of MOEAs. In
this paper, the authors theoretically analyze the MOEAs based on decomposition. First, they analyze the runtime
complexity with two basic simple
instances. In both cases, the Pareto
fronts have one-to-one mapping to the
decomposed sub-problems or not. Second, they analyze the runtime complexity on two difficult instances with
bad neighborhood relations in fitness
space or decision space. Their studies
show that: 1) in certain cases, polynomial sized evenly distributed weight
parameters-based decomposition cannot
map each point in a polynomial sized
Pareto front to a sub-problem; 2) an
ideal serialized algorithm can be very
efficient on some simple instances; 3)
the standard MOEA based on decomposition can benefit a runtime cut of a
constant fraction from its neighborhood
coevolution scheme; and 4) the standard
MOEA based on decomposition performs well on difficult instances because
both the Pareto domination-based and
the scalar sub-problem-based search
schemes are combined in a proper way."
A Survey on Evolutionary Computation
Approaches to Feature Selection, by B.
Xue, M. Zhang, W. N. Browne, and
X.Yao, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 4,
August 2016, pp. 606-626.
Digital Object Identifier: 10.1109/
TEVC.2015.2504420
"Feature selection is an important
task in data mining and machine learning to reduce the dimensionality of the
data and increase the performance of an
algorithm, such as a classification algo-

IEEE CompUtAtIonAl IntEllIgEnCE mAgAzInE | FEBRUARY 2017

rithm. However, feature selection is a
challenging task due mainly to the large
search space. A variety of methods have
been applied to solve feature selection
problems, where evolutionary computation (EC) techniques have recently
gained much attention and shown some
success. However, there are no comprehensive guidelines on the strengths and
weaknesses of alternative approaches.
This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and
successful applications. This paper presents a comprehensive survey of the
state-of-the-art work on EC for feature
selection, which identifies the contributions of these different algorithms. In
addition, current issues and challenges
are also discussed to identify promising
areas for future research."
IEEE Transactions on
Computational Intelligence
and AI in Games

Time Management for Monte Carlo Tree
Search, by H. Baier and M. H. M.
Winands, IEEE Transactions on Computational Intelligence and AI in Games,
Vol. 8, No. 3, September 2016, pp.
301-314.
Digital Object Identifier: 10.1109/
TCIAIG.2015.2443123
"Monte Carlo Tree Search (MCTS)
is a popular approach for tree search in a
variety of games. While MCTS allows
for fine-grained time control, not much
has been published on time management for MCTS programs under tournament conditions. This paper first
investigates the effects of various timemanagement strategies on playing
strength in the challenging game of Go.
A number of domain-independent strategies are then tested in the domains
Connect-4, Breakthrough, Othello, and
Catch the Lion. We consider strategies
taken from the literature as well as
newly proposed and improved ones.
Strategies include both semi-dynamic
strategies that decide about time allocation for each search before it is started,
and dynamic strategies that influence

Table of Contents for the Digital Edition of Computational Intelligence - February 2017