In many real-world optimisation problems, a wide range of uncertainties has to be taken into account.
Generally, uncertainties in evolutionary optimisation can be categorized into four classes:
1. Noisy fitness function.
2. Approximated fitness function.
3. Robustness.
4. Dynamic fitness function.
The EvoSTOC workshop's objective is to foster interest in the issue of handling uncertainties, to provide a forum for researchers to meet, and a platform to present and discuss latest research in the field.
Papers are solicited addressing any of the aforementioned four areas and/or their combination with optimisation methods inspired by nature.
Algorithmic solutions for multi-objective/multi-criteria problems and novel implementation of hybrid (memetic) algorithms are warmly encouraged.
Theoretical and empirical results as well as real-world applications are welcome.
Submission Details:
1. Submissions should be no more than 10 pages.
2. Please use LNCS format
3. As EvoSTOC are adopting a double-blind peer review process, please ensure to anonymise your submission by removing all references to the authors.
4. Submissions should be performed through the conference website
Deadline: 5th November.
A limited amount of Travel Bursaries will be granted to students and researchers from developing countries.
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