A comparative study of the canonical genetic algorithm and a real-valued quantum-inspired evolutionary algorithm

Purpose Following earlier claims that Quantum-inspired Evolutionary Algorithm (QIEA) may offer advantages in high dimensional environments, this paper tests 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. Design/Methodology/Approach – This study compares the performance of both the QIEA and the canonical genetic algorithm on a series of test benchmark functions. Findings The results show that the QIEA obtains highly competitive results when benchmarked against the genetic algorithm within static environments, while substantially outperforming both binary and real-valued representation of the genetic algorithm (GA) in terms of running time. Within dynamic environments, the QIEA outperforms GA in terms of stability and run time. Originality/value This study suggests that QIEA has utility for real-world high dimensional problems, particularly within dynamic environments, such as that found in real-time financial trading.