EvoStar2016

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 EvoSTOC 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. EvoSTOC 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

More information

http://www.evostar.org/2016/

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