@techreport{oai:ipsj.ixsq.nii.ac.jp:00235607,
 author = {Rui, Zhong and Jun, Yu and Masaharu, Munetomo and Rui, Zhong and Jun, Yu and Masaharu, Munetomo},
 issue = {10},
 month = {Jul},
 note = {This paper proposes a novel hyper-heuristic differential evolution (HHDE). We design mutation archive, crossover archive, and boundary repair archive as low-level heuristics of HHDE. A learning-free selection function is employed as the high-level component. Comprehensive numerical experiments on CEC2022 benchmark functions are conducted to assess the efficacy of our proposed HHDE. The performance of HHDE was compared against a range of other state-of-the-art competitor optimizers. The experimental results and statistical analysis confirm the competitiveness and efficiency of HHDE., This paper proposes a novel hyper-heuristic differential evolution (HHDE). We design mutation archive, crossover archive, and boundary repair archive as low-level heuristics of HHDE. A learning-free selection function is employed as the high-level component. Comprehensive numerical experiments on CEC2022 benchmark functions are conducted to assess the efficacy of our proposed HHDE. The performance of HHDE was compared against a range of other state-of-the-art competitor optimizers. The experimental results and statistical analysis confirm the competitiveness and efficiency of HHDE.},
 title = {Hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization},
 year = {2024}
}