Quantum GEP for Dynamic Multiobjective Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Ye:2022:ICNISC,

• author = "Chaoyang Ye and Shicong Zhang and Yisha Liu and Jianhong Lin",
• booktitle = "2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)",
• title = "Quantum {GEP} for Dynamic Multiobjective Optimization",
• year = "2022",
• pages = "845--850",
• abstract = "Gene expression programming, as an evolutionary computing technology, considers the simplicity of genotype and the robustness of function. Combining gene expression programming with quantum evolution method, a quantum gene expression programming QGEP Dynamic algorithm is proposed to solve continuous dynamic multiobjective optimisation problems. The algorithm adapts to environmental changes by making full use of quantum populations to introduce population diversity and affects the search direction of antibody populations for multiobjective GEP, so that antibody populations have better evolutionary ability. The results on three continuous dynamic multiobjective test functions show that the QGEP Dynamic algorithm is superior to traditional genetic algorithm in terms of solution distribution breadth and stability.",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Computers, Heuristic algorithms, Sociology, Antibodies, Dynamic programming, Gene expression, dynamic multiobjective optimisation, quantum evolution",
• DOI = "doi:10.1109/ICNISC57059.2022.00169",
• month = sep,
• notes = "Also known as \cite{10045404}",

}

Genetic Programming entries for Chaoyang Ye Shicong Zhang Yisha Liu Jianhong Lin

Citations