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Best Application papers from the RWA Track will be invited to
submit a revised and enhanced version of the paper to the 'Applied
Soft Computing' Journal. The papers will be reviewed on a 'fast
track' basis. The Journal website: www.elsevier.com/locate/asoc/
Track |
. |
Name |
notes |
AAAA
|
A-Life,
Adaptive Behavior, Agents, and Ant Colony Optimization,
|
Standish,
Russell |
. |
AIS |
Artificial
Immune Systems |
Dasgupta,
Dipankar |
New
this year |
Coev |
Coevolution |
Kendall,
Graham |
New
this year |
DMQ |
DNA,
Molecular, and Quantum Computing |
Jonoska,
Natasha |
Was
DNA before, broadened scope |
EH |
Evolvable
Hardware |
Miller,
Julian |
. |
ER |
Evolutionary
Robotics |
Schultz,
Alan , Potter,
Mitch |
|
ES/EP |
Evolution
Strategies/Evolutionary Programming |
Beyer,
Hans-Georg |
. |
ESR |
Evolutionary
Scheduling and Routing |
Downsland,
Kathryn A. |
|
GA |
Genetic
Algorithms |
Deb,
Kalyanmoy |
. |
GP |
Genetic
Programming |
O'Reilly,
Una-May |
. |
LCS |
Learning
Classifier Systems |
Wilson,
Stewart |
. |
RWA |
Real
World Applications |
Roy,
Rajkumar , Davis,
David |
. |
SBSE |
Search
Based Software Engineering |
Harman,
Mark , Wegner,
Joachim |
|
AAAA,
Alife, Adaptive Behavior, Agents, and Ant Colony Optimization
Standish,
Russell
Recent advancements and applications of Artificial
life, Adaptive behavior, Agents and Ant Colony Optimization
This
special track brings together novel contributions in
the closely related areas of Artificial Life, Adaptive
Behavior, Agents, and Ant Colony Optimization. From
artificial models of biological systems, to the synthesis
of "life" on artificial media; from self-organizing,
self-replicating, and self-learning structures, to bio-inspired
adaptive robots and mobile agents; from collective behaviors
and swarm intelligence, to communication, coordination,
and collaboration in multi-agent teams/colonies; from
organization of agent societies, to applications of
ant colony systems in combinatorial optimization --
this area deals with algorithmic, synthetic, empirical,
and theoretical advances in artificial systems inspired
by evolution, biology, and life.
|
AIS Artificial Immune Systems
Dasgupta, Dipankar
The field of artificial immune systems (AIS) is an emerging
area, which explores and employs different immunological
mechanisms in order to solve computational problems.
This special track will provide a great opportunity
for presenting and disseminating latest work in the
field of Artificial Immune Systems
more
info
|
Coev, Coevolution
Kendall,
Graham
The Coevolution Track welcomes papers that include (but
are not limited) to the following themes:
Artificial
Life
Economics
Game Playing
Game Theory
Negotiation
Neural Networks
Optimization
Robotics
Time Series Prediction
more
info
|
DMQ, DNA, Molecular, and Quantum
Computing
Jonoska,
Natasha
Better
understanding of the molecular world is inspiring and
making possible new ways to do computing. These range
from using DNA and other biological molecules directly
for computation to designing algorithms for quantum computers
that have not yet been built. This track invites papers
about computing at the molecular and quantum level, including
(but not limited to) papers on theoretical algorithms
and applications, analysis of laboratory techniques and
theoretical models, error correction, sequence design,
simulation software, DNA chemistry, DNA motifs and DNA
devices.
|
EH, Evolvable Hardware
Miller,
Julian
Using evolutionary algorithms to design hardware (mechanical
systems, electronic circuits, antennas, etc.)
|
ER, Evolutionary Robotics
Schultz,
Alan , Potter,
Mitch
Automatic
design of autonomous robots using evolutionary algorithms.
Overlapping
and complementary approaches to using evolutionary algorithms
in the areas of robotics have been seen in recent years.
In this special program track, we would like to explore
some of these approaches, and hopefully better understand
their similarities and differences. Topics of interest
include, but are not limited to, the use of evolutionary
algorithms in: development and design of robots, robotic
learning, robotic control, coordination of multiple
robots, perception and multi-sensor integration, and
testing and evaluation of robotic systems.
|
ES/EP,
Evolution Strategies
/ Evolutionary Programming
Beyer,
Hans-Georg
Evolution
Strategies usually evolve solutions in the individual's
natural problem representation (i.e., there is no standard
use of a genotype-phenotype mapping) by application
of mutation and recombination operators and truncation
selection. Papers in this deme would include theoretical
studies of ES (ideally complemented with experiments),
improvements and modifications to the algorithms, and
applications to benchmarking problems (test function
suites, TSP, graph coloring, graph partitioning, etc.)
Evolutionary
Programming was originally proposed to work on finite
state machines, but has lately expanded to a host of
techniques and applications, most of which rely primarilly
on mutation operators, often tailored to the problem
at hand, to explore the search space. Papers in this
deme would include theoretical studies of EP (ideally
complemented with experiments), improvements and modifications
to the algorithms, and applications to benchmarking
problems (test function suites, TSP, graph coloring,
graph partitioning, etc.).
|
ER, Evolutionary Robotics
Schultz,
Alan , Potter,
Mitch
Automatic
design of autonomous robots using evolutionary algorithms.
Overlapping
and complementary approaches to using evolutionary algorithms
in the areas of robotics have been seen in recent years.
In this special program track, we would like to explore
some of these approaches, and hopefully better understand
their similarities and differences. Topics of interest
include, but are not limited to, the use of evolutionary
algorithms in: development and design of robots, robotic
learning, robotic control, coordination of multiple
robots, perception and multi-sensor integration, and
testing and evaluation of robotic systems.
|
SCH, Evolutionary Scheduling
and Routing
Downsland,
Kathryn A.
This
track is concerned with all aspects of evolutionary
and metaheuristic research in scheduling and routing.
A particular aim of the track is to consider evolutionary
research in the field within the context of research
in other meta-heuristics and other approaches from Artificial
Intelligence and Operations Research. Specific aims
of the track include (but are not limited) to the following
themes:
Scheduling Problems and Applications
Meta-heuristic Approaches to Scheduling and Routing
Problems
Vehicle Routing
Travelling Salesman and Related Problems
|
GA,
Genetic Algorithms
Deb,
Kalyanmoy
Genetic
algorithms rely heavily on recombination to explore the
search space, but generally mutation is also used. Papers
in this deme would include theoretical studies of GAs
(ideally complemented with experiments), improvements
and modifications to the algorithms, and applications
to benchmarking problems (test function suites, TSP, graph
coloring, graph partitioning, etc.)
|
GP, Genetic Programming
O'Reilly,
Una-May
Genetic
Programming is the automatic induction of computer programs
and other variable-size structures from a high-level
statement of a problem through evolutionary algorithms.
|
LCS, Learning Classifier
Systems
Wilson,
Stewart
Learning
classifier systems are adaptive rule-based systems that
use an evolutionary algorithm and/or heuristics to search
the space of possible rules.
This
deme is not about decision trees, neural networks, or
other machine learning systems for categorization of
data (Although, of course, classifier systems can be
use to categorize data, and comparisons and CS-ML hybrids
are welcome).
|
RWA, Real World Applications
Davis,
David, Roy,
Rajkumar
Application
of any evolutionary algorithm to problems in the real
world (as opposed to benchmarking problems and test
function suites). The papers in this deme will be peer-reviewed
in the same blind reviewing process of the rest of the
conference, and will be published in the conference
proceedings that will be distributed commercially by
Morgan Kaufmann Publishers. Selected papers of this
track will be invited to submit a revised and enhanced
version to a special issue of the 'Applied Soft Computing'
Journal.
|
SBSE,
Search-based Software Engineering
Harman,
Mark , Wegner,
Joachim
Using evolutionary algorithms and related search
techniques to address problems in software engineering.
Goals of the SBSE track are to
use evolutionary algorithms and related search techniques
to address problems in software engineering
provide definitions of representations, fitness/cost
functions, operators, and search strategies for software
engineering problems
develop and extend the emerging community working
on Search-Based Software Engineering
introduce researchers in evolutionary computation
to problems in software engineering
increase awareness and uptake of evolutionary computation
technology within the software engineering community.
Topics
of interest include requirements engineering, system
and software design, implementation, system and software
integration, quality assurance and testing, project
management, maintenance, change management, optimisation
and transformation as well as development processes.
more
info
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