Computational Intelligence - February 2014 - 16

of the spatial and temporal components of
the data. In this study, the authors revisit
and augment the algorithm for proceeding with the objective function-based
clustering, such as fuzzy C-means, for
making it applicable to spatiotemporal
data. An augmented distance function is
discussed. Besides, the resulting clustering
algorithm is provided. Two optimization
criteria, a reconstruction error and a prediction error, are introduced and used as
vehicles to optimize the performance of
the clustering method. Experimental
results obtained for synthetic and realworld data are reported."
New Stability Conditions Based on
Piecewise Fuzzy Lyapunov Functions
and Tensor Product Transformations, IEEE
Transactions on Fuzzy Systems, Vol. 21,
No. 4, August 2013, pp. 748-760.
Digital Object Identifier: 10.1109/
TFUZZ.2012.2230178
"This paper proposes improvements
of recent stability conditions for wellknown Takagi-Sugeno (T-S) fuzzy systems (continuous-time). The key idea is
to bring together the so-called local
transformations of membership functions and new piecewise fuzzy Lyapunov functions. By relying on these special local transformations, the associated
linear matrix inequalities that are used
to prove the system's stability can be
relaxed without increasing the number
of conditions. In addition, to enhance
the usefulness of the proposed methodology, one can choose between two different sets of conditions characterized
by independence or dependence on
known bounds of the membership
functions time derivatives. A standard
example is presented to illustrate that
the proposed method is able to provide
substantial improvements in some cases."
IEEE Transactions on
Evolutionary Computation

Multimodal Optimization Using a Biobjective Differential Evolution Algorithm
Enhanced with Mean Distance-Based
Selection, by A. Basak, S. Das and KC
Tan, IEEE Transactions on Evolutionary

Computation, Vol. 17, No. 5, October
2013, pp. 666-685.

IEEE Transactions on Computational
Intelligence and AI in Games

Digital Object Identifier: 10.1109/
TEVC.2012.2231685
"Numerous research works integrate
a niching scheme with an existing single-objective evolutionary algorithm to
perfor m multimodal optimization.
Recently a few approaches have recast
multimodal optimization as a multiobjective optimization problem to be
solved by modified multiobjective evolutionary algorithms. In this paper the
authors propose a novel biobjective formulation of the multimodal optimization problem and use differential evolution with nondominated sorting
followed by hypervolume measurebased sorting to finally detect a set of
solutions corresponding to multiple
global and local optima of the function
under test. Their algorithm is compared
with eight state-of-the-art single-objective niching algorithms and two recently
developed biobjective niching algorithms using a test suite of 14 basic and
15 composite multimodal problems."

Games, Gameplay, and BCI: The State
of the Art, by Marshall, D.; Coyle, D.;
Wilson, S.; Callaghan, M., IEEE
Transactions on Computational Intelligence and AI in Games, Vol. 5, No. 2,
June 2013, pp. 82-99.

An Adaptive Particle Swarm Optimization with Multiple Adaptive Methods, by
M. Hu, T. Wu, and J. Weir, IEEE Transactions on Evolutionary Computation, Vol.
17, No. 5, October 2013, pp. 705-720.
Digital Object Identifier: 10.1109/
TEVC.2012.2232931
"An intelligent augmented particle
swarm optimization with multiple
adaptive methods was recently proposed and experiments showed it to be
effective for diverse functions. However, the performance of that algorithm
heavily depends on the settings of three
parameters: the two learning factors
and the inertia weight. In this paper,
the authors propose a parameter control mechanism to adaptively change
those parameters. The performance of
this adaptive algorithm was compared
against several PSO variants and evolutionary algor ithms. The proposed
parameter control method is also compared with several existing parameter
control methods."

IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2014

Digital Object Identifier: 10.1109/
TCIAIG.2013.2263555
"Brain-computer interfaces (BCIs)
and basic computer games have been
interconnected since BCI development
began, exploiting gameplay elements as
a means of enhancing performance in
BCI training protocols and entertaining
and challenging participants while training to use a BCI. BCIs have been used
to enrich the experience of able-bodied
and physically impaired users in various
computer applications, in particular,
computer games. This paper reviews and
evaluates gameplay within BCI. Gameplay is a key aspect of any computer
game and encompasses the challenges
presented to the player, the actions made
available to the player by the game
designer to overcome the challenges and
the interaction mechanism in the game.
The gameplay mechanics employed
across a range of BCI games are
reviewed and evaluated in terms of the
BCI control strategy's suitability, considering the genre and gameplay mechanics employed. A number of recommendations for the field relating to
genre-specific BCI-games development
and assessing user performance are also
provided for BCI game developers."
IEEE Transactions on Autonomous
Mental Development

From Action to Interaction: Infant Object
Exploration and Mothers' Contingent
Responsiveness, by Tamis-LeMonda,
C.S., Kuchirko, Y., and Tafuro, L.,
IEEE Transactions on Autonomous Mental Development, Vol. 5, No. 3, September 2013, pp. 202-209.

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