TY - GEN
T1 - Evolutionary algorithm with 2-crossovers
AU - Xing, Zhihui
AU - Duan, Haibin
AU - Xu, Chunfang
PY - 2009
Y1 - 2009
N2 - Quantum evolutionary algorithm (QEA) is proposed on the basis of the concept and principles of quantum computing, which is a classical meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the principles of evaluation of living organisms in nature. QEA has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid QEA with 2-crossovers was proposed to overcome the above-mentioned limitations. Considering the importance of randomization, 2-crossovers were applied to improve the convergence quality in the basic QEA model. In this way, the new-born individual after each updating can to help the population jump out of premature convergence. The proposed algorithm is tested with the Benchmark optimization problem, and the experimental results demonstrate that the proposed QEA is a feasible and effective in solving complex optimization problems.
AB - Quantum evolutionary algorithm (QEA) is proposed on the basis of the concept and principles of quantum computing, which is a classical meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the principles of evaluation of living organisms in nature. QEA has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid QEA with 2-crossovers was proposed to overcome the above-mentioned limitations. Considering the importance of randomization, 2-crossovers were applied to improve the convergence quality in the basic QEA model. In this way, the new-born individual after each updating can to help the population jump out of premature convergence. The proposed algorithm is tested with the Benchmark optimization problem, and the experimental results demonstrate that the proposed QEA is a feasible and effective in solving complex optimization problems.
KW - Crossover
KW - Genetic algorithm (GA)
KW - Premature
KW - Quantum evolutionary algorithm (QEA)
KW - Qubit chromosome
UR - https://www.scopus.com/pages/publications/69949100904
U2 - 10.1007/978-3-642-01507-6_83
DO - 10.1007/978-3-642-01507-6_83
M3 - 会议稿件
AN - SCOPUS:69949100904
SN - 3642015069
SN - 9783642015069
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 735
EP - 744
BT - Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings
T2 - 6th International Symposium on Neural Networks, ISNN 2009
Y2 - 26 May 2009 through 29 May 2009
ER -