EvoSTOC 2016 - Evolutionary Algorithms and Meta-heuristics in Stochastic and Dynamic Environments

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