A versatile quantum-inspired evolutionary algorithm

This study points out some weaknesses of existing quantum-inspired evolutionary algorithms (QEA) and explains in particular how hitchhiking phenomena can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called versatile quantum- inspired evolutionary algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a classical genetic algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to estimation of distribution algorithms (EDA).

[1]  Jong-Hwan Kim,et al.  On setting the parameters of quantum-inspired evolutionary algorithm for practical application , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[2]  E. Weinberger.  NP Completeness of Kauffman's N-k Model, A Tuneable Rugged Fitness Landscape , 1996 .

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

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

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

[6]  Jong-Hwan Kim,et al.  On the Analysis of the Quantum-inspired Evolutionary Algorithm with a Single Individual , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[7]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[8]  Chang Wook Ahn,et al.  A Memory-Efficient Elitist Genetic Algorithm , 2003, PPAM.

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

[10]  Jong-Hwan Kim,et al.  Face detection using quantum-inspired evolutionary algorithm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[11]  Yan Zhang,et al.  Neural Network Ensemble Based on Vowel Classification for Chinese Speaker Recognition , 2007, Third International Conference on Natural Computation (ICNC 2007).

[12]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[13]  Yan Wang,et al.  Analysis of Gene Expression Data: Application of Quantum-Inspired Evolutionary Algorithm to Minimum Sum-of-Squares Clustering , 2005, RSFDGrC.

[14]  Kyu Ho Park,et al.  A Quantum-Inspired Evolutionary Computing Algorithm for Disk Allocation Method , 2003 .

[15]  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.

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

[17]  Mohamed Batouche,et al.  A Novel Quantum-Inspired Evaluation Algorithm for Multi-Source Affine Image Registration , 2006, ˜The œinternational Arab journal of information technology.

[18]  Nikola Kasabov,et al.  Quantum-Inspired Evolutionary Algorithm: , 2009 .

[19]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[20]  Ganesh K. Venayagamoorthy,et al.  Quantum-inspired Evolutionary Algorithms and Binary Particle Swarm Optimization for Training MLP and SRN Neural Networks , 2005 .

[21]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[22]  Feng Liu,et al.  Wideband Signal DOA Estimation Based on Modified Quantum Genetic Algorithm , 2006, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[23]  Mohamed Batouche,et al.  A Quantum-Inspired Differential Evolution Algorithm for Rigid Image Registration , 2004, International Conference on Computational Intelligence.