EvoSTOC
Evolutionary Algorithms and Meta-heuristics in Stochastic and Dynamic Environments
Important notice
Due to a large number of request for late submissions, the EvoStar submission sites will stay open until this Tuesday 15 November 23:59:59 SST, after which no further submissions will be accepted. Authors who have already submitted, can update their work until this time.
The recipients of the "EvoAPPLICATIONS Best Paper Awards" will be
invited to submit an extended version of their works to a special
issue of Memetic Computing.
Following the success of previous events and the importance of the
field of evolutionary and bio-inspired computation for dynamic
optimization problems, the Evo
STOC 2017 will run its 14th edition
as a track of EvoApplications, the 20th European Conference on the
Applications of Evolutionary and bio-inspired Computation.
PUBLICATION DETAILS
Submissions will be rigorously reviewed by at least three members
of the program committee. Accepted papers will be presented orally
at the event and included in the EvoApplications proceedings,
published by Springer Verlag in a dedicated volume of the Lecture
Notes in Computer Science series. Submissions must be original and
not published elsewhere. The submissions will be peer reviewed.
The authors of accepted papers will have to improve their paper on
the basis of the reviewers’ comments and will be asked to send a
camera-ready version of their manuscripts. At least one author of
each accepted work has to register for the conference and attend the
conference and present the work.
TOPICS OF INTEREST
Many real-world optimisation problems are characterised by some
types of uncertainty that need to be accounted for by the algorithms
used to solve the problems. These uncertainties include noise (noisy
optimisation), approximations (surrogate-assisted optimisation),
dynamics (dynamic/online optimisation problems) as well as the
requirements for robust solutions (robust optimisation). Dealing with
these uncertainties has become increasingly popular in stochastic
optimisation in recent years and a variety of new techniques have
been proposed. The objective of Evo
STOC is to foster interest in
metaheuristics and stochastic optimisation for stochastic and dynamic
environments and to provide an opportunity for researchers to meet
and to present and discuss the state-of-the-arts in the field.
Evo
STOC accepts contributions, both empirical and theoretical in
nature, for any work relating to nature-inspired, metaheuristics and
stochastic techniques applied to a domain characterised by one or
more types of uncertainty.
Topics of interest include, but are not limited to, any of the
followings in the realm of nature-inspired, metaheuristics and
stochastic computation:
- noisy fitness functions
- fitness approximations / surrogate-assisted optimisation
- robust solutions and robust optimisation
- dynamic optimisation problems
- dynamic constrained optimisation problems
- dynamic multi-objective optimisation problems
- co-evolutionary domains
- online optimisation
- online learning
- big data analysis in dynamic environments
- dynamic and robust optimisation benchmark problems
- real-world applications characterised by uncertainty and online real-world applications
- the applications of nature-inspired, metaheuristics and stochastic optimisation on vulnerability and risk analysis/management
- the applications of nature-inspired, metaheuristics and stochastic optimisation on reliability and robustness of real-world systems
- optimisation in (video) games and related domains (e.g., dynamical systems)
- theoretical results (e.g., runtime analysis) for stochastic problems
Additional information and submission details
Submit your manuscript, at most 16 A4 pages long, in Springer LNCS
format (instructions downloadable from
http://www.springer.com/
computer/lncs?SGWID=0-164-6-793341-0).
Page limit: 16 pages
The reviewing process will be double-blind; please omit information
about the authors in the submitted paper.
Further information on the conference and co-located events can be
found in: http://www.evostar.org
Track chairs
- Trung Thanh Nguyen
Liverpool John Moores University, United Kingdom
T.T.Nguyen(at)ljmu.ac.uk
-
Michalis Mavrovouniotis
Nottingham Trent University, United Kingdom
michalis.mavrovouniotis(at)ntu.ac.uk;