Special Session on Evolutionary Computation in Dynamic
and Uncertain Environments
ECiDUE'13
Many real-world optimization problems are subjected to dynamism
and uncertainties that are often impossible to avoid in practice.
For instance, the fitness function is uncertain or noisy as a result
of simulation /measurement errors or approximation errors (in the
case where surrogates are used in place of the computationally
expensive high fidelity fitness function). In addition, the design
variables or environmental conditions can be perturbed or they
change over time.
The tools to solve these dynamic and uncertain optimization
problems (DOP) should be flexible, able to tolerate uncertainties,
fast to allow reaction to changes and adaptive. Moreover, the
objective of such tools is no longer to simply locate the global
optimum solution, but to continuously track the optimum in dynamic
environments, or to find a robust solution that operates properly in
the presence of uncertainties.
The last decade has witnessed increasing research efforts on
handling dynamic and uncertain optimization problems using
evolutionary algorithms and other metaheuristics, and a variety of
methods have been reported across a broad range of application
backgrounds.
This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field.