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Jürgen Branke

Evolutionary Optimization in Dynamic Environments

Many real-world optimization problems are dynamic and changing over time. In the thesis discussed here, it is shown how evolutionary algorithms can be adapted to work well in dynamic environments. Four aspects are treated: 1. How to continuously track a changing optimum over time. 2. How to trade-off solution quality and adaptation cost. 3. How to find robust solutions, whose quality is insensitive to changes in the environment. 4. How to find flexible solutions, which are not only good but can be easily adapted when necessary.

it – Information Technology (vormals it+ti), Oldenbourg Wissenschaftsverlag

Print ISSN: 1611-2776
Volume: 45, 03/2003
Pages: 170

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