Alternative Restart Strategies for CMA-ES

This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.


Published in:
12th International Conference on Parallel Problem Solving From Nature, 296-305
Presented at:
12th International Conference on Parallel Problem Solving From Nature
Year:
2012
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 Record created 2013-04-18, last modified 2018-03-17

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