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, 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.Design/methodology/approach – This paper compares the performance of both the QIEA and the canonical genetic algorithm (GA) on a series of test benchmark functions.Findings – 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.Originality/value – This paper suggests that QIEA has utility for real‐world high‐dimensional problems, particularly within dynamic environments, such as that found in real‐time f...

[1]  Jong-Hwan Kim,et al.  Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion , H Gate , and Two-Phase Scheme , 2009 .

[2]  M. Pacheco,et al.  Quantum-Inspired Evolutionary Algorithm for Numerical Optimization , 2006 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Dirk Thierens,et al.  Multi-objective mixture-based iterated density estimation evolutionary algorithms , 2001 .

[5]  Shengxiang Yang,et al.  Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

[7]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[8]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[9]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[10]  Anthony Brabazon,et al.  Benchmarking the performance of the real-valued Quantum-inspired Evolutionary Algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[11]  Shuyuan Yang,et al.  A novel quantum evolutionary algorithm and its application , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[13]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[14]  Hartmut Pohlheim,et al.  Genetic and evolutionary algorithm toolbox for use with matlab , 1994 .

[15]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling , 2006, Studies in Computational Intelligence.

[16]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[17]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[18]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[19]  Anthony Brabazon,et al.  Testing a Quantum-inspired Evolutionary Algorithm by applying it to Non-linear Principal Component Analysis of the Implied Volatility Smile , 2007 .

[20]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[21]  David E. Goldberg,et al.  Multi-objective bayesian optimization algorithm , 2002 .

[22]  Anthony Brabazon,et al.  Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm , 2007, GECCO '07.

[23]  Zengqi Sun,et al.  A New Approach Belonging to EDAs: Quantum-Inspired Genetic Algorithm with Only One Chromosome , 2005, ICNC.

[24]  Ajit Narayanan,et al.  Quantum-inspired genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[25]  H. Muhlenbein,et al.  The Factorized Distribution Algorithm for additively decomposed functions , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[26]  Anthony Brabazon,et al.  Biologically inspired algorithms for financial modelling , 2006, Natural computing series.

[27]  L. Jiao,et al.  A genetic algorithm based on quantum chromosome , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[28]  Marley M. B. R. Vellasco,et al.  Quantum-Inspired Evolutionary Algorithm for Numerical Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[29]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[30]  Ponnuthurai Nagaratnam Suganthan,et al.  Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization , 2008 .

[31]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .

[32]  Anthony Brabazon,et al.  Natural Computing in Computational Finance , 2008, Natural Computing in Computational Finance.

[33]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme , 2004, IEEE Transactions on Evolutionary Computation.