A restart CMA evolution strategy with increasing population size
A restart CMA evolution strategy with increasing population size
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DOI：
10.1109/cec.2005.1554902
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发表时间：
2005-12
期刊：
影响因子：
--
通讯作者：
A. Auger;N. Hansen
中科院分区：
文献类型：
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作者：
A. Auger;N. Hansen
在本文中，我们介绍了一个restart-CMA-evolution策略，其中人口规模增加每次重新启动（IPOP）。通过增加种群规模，搜索特性在每次重启后变得更加全局化。IPOP-CMA-ES是在为CEC 2005实参数优化专题会议设计的25个功能的测试套件上进行评估的。它的性能相比，一个本地的重新启动策略与恒定的小人口规模。在单峰函数上，性能类似。在多模态函数上，本地重启策略在4个测试用例中显着优于IPOP，而IPOP在60个测试用例中的29个测试用例中表现显着更好。
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.