A comparative study of the canonical genetic algorithm and a real-valued quantum-inspired evolutionary algorithm. (English) Zbl 1185.68831
Summary: Following earlier claims that quantum-inspired evolutionary algorithm (QIEA) may offer advantages in high-dimensional environments, the purpose of this paper is to test a real-valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability within both static and dynamic environments.
This paper compares the performance of both the QIEA and the canonical Genetic Algorithm (GA) on a series of test benchmark functions.
The results show that the QIEA obtains highly competitive results when benchmarked against the GA within static environments, while substantially outperforming both binary and real-valued representation of the GA in terms of running time. Within dynamic environments, the QIEA outperforms GA in terms of stability and run time.
This paper suggests that QIEA has utility for real-world high-dimensional problems, particularly within dynamic environments, such as that found in real-time financial trading.
This paper compares the performance of both the QIEA and the canonical Genetic Algorithm (GA) on a series of test benchmark functions.
The results show that the QIEA obtains highly competitive results when benchmarked against the GA within static environments, while substantially outperforming both binary and real-valued representation of the GA in terms of running time. Within dynamic environments, the QIEA outperforms GA in terms of stability and run time.
This paper suggests that QIEA has utility for real-world high-dimensional problems, particularly within dynamic environments, such as that found in real-time financial trading.
MSC:
| 68W05 | Nonnumerical algorithms |
| 68Q12 | Quantum algorithms and complexity in the theory of computing |
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
References:
| [1] | Fan, K., Brabazon, A., O’Sullivan, C. and O’Neill, M. (2008a), ”Benchmarking the performance of the real-valued quantum-inspired evolutionary algorithm”, Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC), Hong Kong, June 1-6, pp. 3092-8. |
| [2] | DOI: 10.1109/TEVC.2002.804320 · doi:10.1109/TEVC.2002.804320 |
| [3] | Han, K.-H. and Kim, J.-H. (2002b), ”Quantum-inspired evolutionary algorithms with a new termination criterion, hgate and two-phase scheme”, IEEE Transactions on Evolutionary Computation, Vol. 8 No. 3, pp. 156-69. |
| [4] | Jeremy, S., de Bonet, C.L. and Isbell, P.V. (1997), ”MIMIC: finding optima by estimating probability densities”, Advances in Neural Information Processing System, Vol. 9, pp. 424-31. |
| [5] | DOI: 10.1162/evco.1997.5.3.303 · doi:10.1162/evco.1997.5.3.303 |
| [6] | DOI: 10.1162/106365600750078808 · doi:10.1162/106365600750078808 |
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