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Self-adaptation of mutation rates in non-elitist populations (2016)
Conference Proceeding
Lehre, P. K., & Dang, D. (2016). Self-adaptation of mutation rates in non-elitist populations. In Parallel problem solving from nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, proceedingsdoi:10.1007/978-3-319-45823-6_75

The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experime... Read More

Populations can be essential in tracking dynamic optima (2016)
Journal Article
Dang, D., Jansen, T., & Lehre, P. K. (in press). Populations can be essential in tracking dynamic optima. Algorithmica, doi:10.1007/s00453-016-0187-y. ISSN 0178-4617

Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisatio... Read More

Runtime analysis of non-elitist populations: from classical optimisation to partial information (2016)
Journal Article
Dang, D., & Lehre, P. K. (2016). Runtime analysis of non-elitist populations: from classical optimisation to partial information. Algorithmica, 75(3), doi:10.1007/s00453-015-0103-x. ISSN 0178-4617

Although widely applied in optimisation, relatively little has been proven rigorously about the role and behaviour of populations in randomised search processes. This paper presents a new method to prove upper bounds on the expected optimisation time... Read More