@techreport{oai:ipsj.ixsq.nii.ac.jp:00194996,
 author = {Xu, Han and Takahiro, Shinozaki and Ryota, Kobayashi and Xu, Han and Takahiro, Shinozaki and Ryota, Kobayashi},
 issue = {9},
 month = {Mar},
 note = {Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is considered as one of the most effective method for black-box optimization issue. In this paper, we apply CMA-ES to the neuron model parameter optimization problem, and compare it with genetic algorithm (GA) and the Nelder-Mead method which are the widely used approaches. To enhanced robustness of CMA-ES, we extend it by making an aggregation of evolution. We analyze a public dataset recorded from a rat neocortical neuron, which shows that the proposed approach achieves higher performance than the conventional methods., Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is considered as one of the most effective method for black-box optimization issue. In this paper, we apply CMA-ES to the neuron model parameter optimization problem, and compare it with genetic algorithm (GA) and the Nelder-Mead method which are the widely used approaches. To enhanced robustness of CMA-ES, we extend it by making an aggregation of evolution. We analyze a public dataset recorded from a rat neocortical neuron, which shows that the proposed approach achieves higher performance than the conventional methods.},
 title = {Aggregated CMA-ES: An Effective and Stable Strategy for Neuron Model Optimization},
 year = {2019}
}