TY - GEN
T1 - A classification and Pareto domination based multiobjective evolutionary algorithm
AU - Zhang, Jinyuan
AU - Zhou, Aimin
AU - Zhang, Guixu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - In multiobjective evolutionary algorithms, most selection operators are based on the objective values or the approximated objective values. It is arguable that the selection in evolutionary algorithms is a classification problem in nature, i.e., selection equals to classifying the selected solutions into one class and the unselected ones into another class. Following this idea, we propose a classification based preselection for multiobjective evolutionary algorithms. This approach maintains two external populations: one is a positive data set which contains a set of 'good' solutions, and the other is a negative data set contains a set of 'bad' solutions. In each generation, the two external populations are used to train a classifier firstly, then the classifier is applied to filter the newly generated candidate solutions and only the ones labeled as positive are kept as the offspring solutions. The proposed preselection is integrated into the Pareto domination based algorithm framework in this paper. A systematic empirical study on the influence of different classifiers and different reproduction operators has been done. The experimental results indicate that the classification based preselection can improve the performance of Pareto domination based multiobjective evolutionary algorithms.
AB - In multiobjective evolutionary algorithms, most selection operators are based on the objective values or the approximated objective values. It is arguable that the selection in evolutionary algorithms is a classification problem in nature, i.e., selection equals to classifying the selected solutions into one class and the unselected ones into another class. Following this idea, we propose a classification based preselection for multiobjective evolutionary algorithms. This approach maintains two external populations: one is a positive data set which contains a set of 'good' solutions, and the other is a negative data set contains a set of 'bad' solutions. In each generation, the two external populations are used to train a classifier firstly, then the classifier is applied to filter the newly generated candidate solutions and only the ones labeled as positive are kept as the offspring solutions. The proposed preselection is integrated into the Pareto domination based algorithm framework in this paper. A systematic empirical study on the influence of different classifiers and different reproduction operators has been done. The experimental results indicate that the classification based preselection can improve the performance of Pareto domination based multiobjective evolutionary algorithms.
KW - Evolutionary algorithm
KW - classification
KW - multiobjective optimization
KW - preselection
UR - https://www.scopus.com/pages/publications/84963596903
U2 - 10.1109/CEC.2015.7257247
DO - 10.1109/CEC.2015.7257247
M3 - 会议稿件
AN - SCOPUS:84963596903
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 2883
EP - 2890
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -