Self-learning Genetic Algorithm for Neural Network Topology Optimization

Author(s):  
Radomir Perzina
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Maoqing Zhang ◽  
Lei Wang ◽  
Zhihua Cui ◽  
Jiangshan Liu ◽  
Dong Du ◽  
...  

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.


Author(s):  
Adrian Carballal ◽  
Francisco Cedron ◽  
Iria Santos ◽  
Antonino Santos ◽  
Juan Romero

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