Computational Intelligence - February 2015 - 9

IEEE Computational Intelligence
Magazine Outstanding Paper Award

"A Unified Framework for Symbiosis of
Evolutionary Mechanisms with Application to Water Clusters Potential Model
Design" by Minh Nghia Le, Yew Soon
Ong, Yaochu Jin and Bernhard Sendhoff,Vol. 7, No. 1, pp. 20-35, 2012.
Outstanding Chapter Award

IEEE CIS Hefei Chapter, CHINA
For promoting and supporting computational
intelligence research and applications through
a number of technical activities, including free
workshops and seminars.
Outstanding Organization Award

Lar us Technolog ies Cor poration,
CANADA
For advancing the field of computational
intelligence as applied to defense and security
applications.

Larus Technologies Corporation, located in Ottawa, Canada is a wholly
owned Canadian research and engineering company. Larus is comprised of
three core business areas, Data Fusion
and Analytics Solutions, including the
patented Total::Insight™ Decision
Support System (DSS), Software
Engineering Consulting, primarily in
the area of Command, Control, Communications, Computers, Intelligence,
Surveillance and Reconnaissance
(C4ISR) for public secur ity and
defense, and Research and Engineering,
focusing on innovations in Computational Intelligence. Larus Technologies
has become known for its expertise in
developing solutions and advanced
decision support systems requiring
multi-sensor data collection, aggregation, exploitation and fusion.
Larus specializes in the areas of network-enabled real-time systems, simulation environments, embedded computer
and control systems. An extensive back-

ground in public and private security
projects has made Larus a specialist in
high reliability, real-time systems with
adherence to standards and quality.
Larus is an innovator of next-generation embedded technology for developers of mission-critical C4ISR and
Security systems with a solid foundation pioneering high level information
fusion for the ever-changing defense
and security industries.
Total::Insight uses Computational
Intelligence, advanced learning techniques, prediction and assessment capabilities to combine and analyze the
information from a variety of sources
including passive and active sensors,
existing trackers and correlators, as well
as from a multitude of other data sources (e.g. weather reports, operator
repor ts, web pages). This allows
Total::Insight to sift through vast
amounts of data, capture complex relations which result in the learning and
the real-time recognition of patterns,
events and anomalies in order to always
produce accurate information and to
continually optimize situational awareness for decision makers.
Total::Insight includes the Risk
Management Framework (RMF) designed
to extract risk features in real-time
from a variety of assets in order to
detect, well ahead of time, which assets
are more likely headed for distress in
order to rapidly plan the optimal
response (out of many possibilities), and
the ISR Multi-Asset Optimizer allowing
mission planners to rapidly generate a
wide variety of optimized mission/collection plans for air, land and sea based
search-and-rescue (SAR) assets. The
optimizer was developed to ensure
optimal use of response assets in order
to meet critical mission objectives
while at the same time fulfilling multiple and often challenging, mission constraints. Larus continues to innovate in
the field of Computational Intelligence
extending Total::Insight to address
additional decision support requirements and support a wider variety of
hard and soft data sources. For additional information, see http://www.
larus.com.

Outstanding PhD Dissertation Award

Yong Wang for his
thesis entitled
" E vo l u t i o n a r y
Al g or it hm s f o r
Complex Continuous Optimization
Problems" presented for the degree
of Doctor of Philosophy in the Central
South University, China, May 2011.
Supervisor: Zixing Cai, Central
South University, China.
Abstract-This thesis aims at
solving four kinds of complex continuous optimization problems by evolutionary algor ithms: unconstrained
single-objective optimization problems (USOPs), constrained singleobjective optimization problems
(CSOPs), unconstrained multi-objective optimization problems (UMOPs),
and constrained multi-objective optimization problems (CMOPs). Firstly, a
composite differential evolution (DE)
and a generic DE framework have
been proposed to enhance the convergence speed and robustness of DE for
complex continuous USOPs. Secondly, this thesis carries out an in-depth
analysis on the constraint-handling
techniques based on multi-objective
optimization. Moreover, a series of
methods has been designed for complex continuous CSOPs. Thirdly, a
reducing redundant cluster operator
has been proposed to address an open
issue in the regularity model-based
multi-objective estimation of distribution algorithm, i.e., how to determine
the number of clusters in the modelling. Finally, a generalized adaptive
tradeoff model has been proposed for
complex continuous CMOPs. The
methods presented in this thesis have
been successfully applied to solve
optimization problems in more than
20 fields by other research groups. The
CoDE algorithm is the core technology of a commercial software bSLOPE
developed by O.C. Rickard and N.
Sitar in the University of California,
Berkeley. The source codes of all the
proposed methods have been made
available on the internet.

february 2015 | Ieee ComputatIonal IntellIgenCe magazIne

http://www http://www.larus.com

Table of Contents for the Digital Edition of Computational Intelligence - February 2015