Title :
Function optimization by RPLNN
Author :
Menhaj, Mohammad B. ; Seifipour, Navid
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
fDate :
6/24/1905 12:00:00 AM
Abstract :
This paper introduces a model-free optimization method, called ring probabilistic logic neural networks (RPLNNs), for function optimization. In order to compare the performance of RPLNNs with that of conventional genetic algorithms (CGAs), two different optimization problems have been considered. The simulation results show that the RPLNN remarkably outperforms the CGA and some gradient-based methods as well
Keywords :
functional analysis; mathematics computing; neural nets; optimisation; performance evaluation; probabilistic logic; function optimization; genetic algorithms; gradient-based methods; model-free optimization method; performance; ring probabilistic logic neural networks; simulation; Genetic algorithms; Iterative algorithms; Iterative methods; Neural networks; Neurons; Optimization methods; Probabilistic logic; Search methods; Simulated annealing; Solids;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007743
