Research on Multi-population Quantum Genetic Algorithm Based on Optimal Computation Allocation Technology

Ziyuan Guo
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:421-426, 2025.

Abstract

Quantum genetic algorithms have proven their unique superiority in dealing with stochastic optimization problems. In this paper, we propose an innovative multi-population quantum genetic algorithm, which is based on optimal computational resource allocation techniques. By carefully optimizing the initialization strategy of the population and introducing the concept of an elite population, combined with optimal computational resource allocation techniques, we have significantly improved the performance of the algorithm on stochastic optimization problems. After a series of experimental verifications, we found that the proposed algorithm surpasses traditional quantum genetic algorithms and other classical optimization algorithms in terms of convergence speed and solution accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-guo25a, title = {Research on Multi-population Quantum Genetic Algorithm Based on Optimal Computation Allocation Technology}, author = {Guo, Ziyuan}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {421--426}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/guo25a/guo25a.pdf}, url = {https://proceedings.mlr.press/v278/guo25a.html}, abstract = {Quantum genetic algorithms have proven their unique superiority in dealing with stochastic optimization problems. In this paper, we propose an innovative multi-population quantum genetic algorithm, which is based on optimal computational resource allocation techniques. By carefully optimizing the initialization strategy of the population and introducing the concept of an elite population, combined with optimal computational resource allocation techniques, we have significantly improved the performance of the algorithm on stochastic optimization problems. After a series of experimental verifications, we found that the proposed algorithm surpasses traditional quantum genetic algorithms and other classical optimization algorithms in terms of convergence speed and solution accuracy.} }
Endnote
%0 Conference Paper %T Research on Multi-population Quantum Genetic Algorithm Based on Optimal Computation Allocation Technology %A Ziyuan Guo %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-guo25a %I PMLR %P 421--426 %U https://proceedings.mlr.press/v278/guo25a.html %V 278 %X Quantum genetic algorithms have proven their unique superiority in dealing with stochastic optimization problems. In this paper, we propose an innovative multi-population quantum genetic algorithm, which is based on optimal computational resource allocation techniques. By carefully optimizing the initialization strategy of the population and introducing the concept of an elite population, combined with optimal computational resource allocation techniques, we have significantly improved the performance of the algorithm on stochastic optimization problems. After a series of experimental verifications, we found that the proposed algorithm surpasses traditional quantum genetic algorithms and other classical optimization algorithms in terms of convergence speed and solution accuracy.
APA
Guo, Z.. (2025). Research on Multi-population Quantum Genetic Algorithm Based on Optimal Computation Allocation Technology. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:421-426 Available from https://proceedings.mlr.press/v278/guo25a.html.

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