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CALL FOR PAPERS
Massively Parallel and Distributed Evolutionary Computation
IEEE CEC 2017 Special Session
June 20-23, 2017, Donostia - San Sebastián, Spain
https://sites.google.com/site/bilelderbelpro/home/mpdec-cec2017
SUBMISSION DEADLINE: January 16, 2017
OVERVIEW:
Parallel
and distributed computing systems have come a long way from specialized
big-scale computer systems such as Grids and clusters. Nowadays,
multi-core processing is present in our desktop systems and smartphones
as well as other mod cons. Parallel and distributed computing systems
have also moved from being permanent, physical and synchronized systems
to being used in an ad hoc, temporal and virtual (cloud) asynchronous
manner. Thus, the adaptation of evolutionary algorithms of any kind to
these environments presents unique challenges from many points of views:
from the purely theoretical that studies the influence of different
types of communication among populations, to the practical that intends
to predict the performance of the parallel system or apply it to a
particular problem. Additionally, the challenges of nowadays
optimization problems can be characterized following different complex
and cross-dependent aspects: a large number of decision variables, a
large number of conflicting objectives, expensive evaluation functions,
simulation-dependent problem formulations, uncertain and scenario-based
models, multi-disciplinary models, non-smooth and multi-modal black-box
setting, etc. These characteristics give rise to difficult issues being
beyond the ability of commonly used optimization algorithms. In this
respect, there is evidence that decentralized evolutionary computing and
general purpose metaheuristics will play a crucially important role in
order to foster the next generation optimization techniques and to
accelerate their widespread uptake.
TOPICS OF INTEREST:
This
special session aims at fostering the cross-fertilization of knowledge
between evolutionary algorithms, or metaheuristics in general, and
parallel, distributed and concurrent computing, in order to address
increasingly complex and large scale optimization problems. Working in
two domains of research can be hard, but the cross-fertilization might
be fruitful. Knowledge about parallel computing helps in creating
parallel algorithms for clouds, multi-core or GPU architectures.
However, this also implies the need for a careful definition of proper
benchmarks, software tools, and metrics to measure the behavior of
algorithms in a meaningful way. In concrete, a conceptual separation
between physical parallelism and decentralized algorithms (whether
implemented in parallel or not) is needed to better analyze the
resulting algorithms.
This special session is expected to collect
contributions, from the theory through the implementation, to the
application of techniques born from the crossover with metaheuristics of
the traditional research fields in parallel computing. Articles are
solicited, that describe significant and methodologically well-founded
contributions to problem solving, aimed at maximizing both efficiency
and accuracy.
This special session includes topics concerning the
design, implementation, and application of parallel evolutionary
algorithms, as well as metaheuristics in general, for solving single- or
multi- objective optimization problems. Potential topics include, but
are not limited to:
- Parallel/distributed/concurrent (PDC)
evolutionary, memetic, dynamic algorithms and metaheuristics, for
single- and multi- objective combinatorial and continuous problems
-
Decentralized evolutionary optimization techniques and paradigms with
clear parallel potential for big optimization problems, e.g.,
divide-and-conquer techniques, aggregation and grouping-based
algorithms, novel decomposition-based techniques in decision and
objective space, novel parallel models for large scale optimization
- Parallel/distributed/concurrent (PDC) computing models and/or their realizations in practice: cloud, P2P, browser-based, socket-based, mobile, etc
- Tools for helping in designing new parallel algorithms, PDC software frameworks/libraries
- PDC test benchmarks, performance evaluation and scalability issues
- Theory of PDC evolutionary algorithms and metaheuristics
- Big data and cloud computing
- MapReduce implementations of evolutionary computation or swarm intelligence approaches
- Real-world applications and computational investigations on the solving of big optimization problems
SUBMISSION:
Please
follow the IEEE CEC2017 instruction for authors and submit your paper
via the IEEE CEC 2017 online submission system (Submission Web Site).
Please specify that your paper is for the Special Session on Massively
Parallel and Distributed Evolutionary Computation.
IMPORTANT DATES:
Paper submission: January 16, 2017
Notification of acceptance: February 26, 2017
Final paper submission: March 12, 2017
Conference dates: June 5-8, 2017
ORGANIZERS:
Mathieu Brévilliers, Université de Haute-Alsace, France
Bilel Derbel, University of Lille, France
Lhassane Idoumghar, Université de Haute-Alsace, France
Julien Lepagnot, Université de Haute-Alsace, France
Simone Ludwig, North Dakota State University, USA
JJ Merelo, University of Granada, Spain
Qingfu Zhang, City University, Hong Kong
