A restart CMA evolution strategy with increasing population size
In this paper we introduce a restart-CMA-evolution strategy, where the population size is
increased for each restart (IPOP). By increasing the population size the search characteristic
becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of
25 functions designed for the special session on real-parameter optimization of CEC 2005.
Its performance is compared to a local restart strategy with constant small population size.
On unimodal functions the performance is similar. On multi-modal functions the local restart�…
increased for each restart (IPOP). By increasing the population size the search characteristic
becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of
25 functions designed for the special session on real-parameter optimization of CEC 2005.
Its performance is compared to a local restart strategy with constant small population size.
On unimodal functions the performance is similar. On multi-modal functions the local restart�…
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.
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