Computational Intelligence - August 2017 - 7

dynamics parameters of the exoskeleton
robot. Extensive experiment studies
employing three subjects have been
-performed to verify the validity of the
proposed method."
Assessing a Fuzzy Extension of Rand
Index and Related Measures, by A.
Suleman, IEEE Transactions on Fuzzy
Systems, Vol. 25, No. 1, February
2017, pp. 237-244.
Digital Object Identifier: 10.1109/
TFUZZ.2016.2554155
"This empirical study examines the ability of a generalization of the Rand index
and four related measures of similarity to
recover the cluster structure of the data in
the framework of fuzzy c-means clustering.
The index range is also used as a criterion
statistic. A Monte Carlo simulation is conducted for both the null case and where the
data have a well-defined cluster structure.
The fuzzy extension of the related measures
is not so effective for imbalanced data. On
the contrary, whether the index is Dice,
Fowlkes and Mallows, Hurbert and Arabie,
or Jaccard, it provides reliable results for
noise data or for data containing fairly balanced clusters. The criticisms of the Rand
index in the context of crisp clustering can
also be extended to its fuzzy version."
IEEE Transactions on
Evolutionary Computation

Efficient Use of Partially Converged Simulations in Evolutionary Optimization,
by J. Branke, M. Asafuddoula, K. S.
Bhattacharjee, and T. Ray, IEEE
Transactions on Evolutionary Computation, Vol. 21, No. 1, February 2017,
pp. 52-64.
Digital Object Identifier: 10.1109/
TEVC.2016.2569018
"For many real-world optimization
problems, evaluating a solution involves
running a computationally expensive
simulation model. This makes it challenging to use evolutionary algorithms
that usually have to evaluate thousands
of solutions before converging. On the
other hand, in many cases, even a prematurely stopped run of the simulation

may serve as a cheaper, albeit less accurate (low fidelity), estimate of the true
fitness value. For evolutionary optimization, this opens up the opportunity to
decide about the simulation run length
for each individual. This paper proposes
a mechanism that is capable of learning
the appropriate simulation run length
for each solution. To test the approach, it
proposes two new benchmark problems,
one simple artificial benchmark function
and one benchmark based on a computational fluid dynamics (CFDs) simulation scenario to design a toy submarine.
As it demonstrates, the proposed algorithm finds good solutions much more
quickly than always using the full CFDs
simulation and provides much better
solution quality than a strategy of progressively increasing the fidelity level
over the course of optimization."
Many-Objective Evolutionary Algorithms
Based on Coordinated Selection Strategy,
by Z. He and G. G.Yen, IEEE Transactions on Evolutionary Computation, Vol.
21, No. 2, April 2017, pp. 220-233.
Digital Object Identifier: 10.1109/
TEVC.2016.2598687
"Selection strategy, including mating
selection and environmental selection, is a
key ingredient in the design of evolutionary
multiobjective optimization algorithms.
Existing approaches, which have shown
competitive performance in low-dimensional multiobjective optimization problems
with two or three objectives, often encounter considerable challenges in many-objective optimization, where the number of
objectives exceeds 3. This paper first provides a comprehensive analysis on the selection strategies in the current evolutionary
many-objective optimization algorithms.
Afterward, it proposes a coordinated selection strategy to improve the performance of
evolutionary algorithms in many-objective
optimization.This selection strategy considers three crucial factors: 1) the new mating
selection criterion considers both the quality of each selected parent and the effectiveness of the combination of selected parents;
2) the new environmental selection criterion directly focuses on the performance of
the whole population rather than single

individual alone; and 3) both selection steps
are complement to each other and the
coordination between them in the evolutionary process can achieve a better performance than each of them used individually.
Furthermore, in order to handle the curse
of dimensionality in many-objective optimization problems, a new convergence
measure by distance and a new diversity
measure by angle are developed in both
selection steps. Experimental results on both
DTLZ and WFG benchmark functions
demonstrate the superiority of the proposed
algorithm in comparison with six state-ofthe-art designs in terms of both solution
quality and computational efficiency."
IEEE Transactions on Computational
Intelligence and AI in Games

Rapid Skill Capture in a First-Person
Shooter, by D. Buckley, K. Chen, and J.
Knowles, IEEE Transactions on Computational Intelligence and AI in Games,
Vol. 9, No. 1, March 2017, pp. 63-75.
Digital Object Identifier: 10.1109/
TCIAIG.2015.2494849
"Various aspects of computer game
design, including adaptive elements of
game levels, characteristics of "bot" be--
havior, and player matching in multiplayer games, would ideally be sensitive to a
player's skill level. Yet, while game difficulty and player learning have been
explored in the context of games, there
has been little work analyzing skill per se,
and how this is related to the interaction
of a player with the controls of the game-
the player's input. To this end, we present
a data set of 476 game logs from over
40 players of a first-person shooter game
(Red Eclipse) as a basis of a case study.
We then extract features from the keyboard and mouse input and provide an
analysis in relation to skill. Finally, we
show that a player's skill can be predicted
using less than a minute of their keyboard
presses. We suggest that the techniques
used here are useful for adapting games
to match players' skill levels rapidly, arguably more rapidly than solutions based on
performance averaging such as TrueSkill."
(continued on page 9)

AUGUST 2017 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

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