Title :
A new evolutionary computation method
Author :
Yan, WeI ; Zhu, Zhaoda
Author_Institution :
Dept. of Electron. Eng., Nanjing Univ., China
Abstract :
A real-valued genetic algorithm is proposed to the optimization problem with continuous variables. It is composed of a simple and general-purpose dynamic scaled fitness and selection operator, real-valued crossover operator, mutation operators and adaptive probabilities for these operators. The proposed algorithm is tested by two generally used functions and is applied to the training of a neural network for image recognition. Experimental results show that the proposed algorithm is an efficient global optimization algorithm
Keywords :
image recognition; learning (artificial intelligence); mathematical operators; neural nets; optimisation; simulated annealing; adaptive operator; adaptive probabilities; continuous variables; dynamic scaled fitness and selection operator; efficient global optimization algorithm; elitist selection strategy; evolutionary computation method; function optimization; image recognition; mutation operators; neural network; optimization problem; real-valued crossover operator; real-valued genetic algorithm; simple general-purpose operator; training; Evolutionary computation; Frequency conversion; Frequency diversity; Genetic algorithms; Genetic mutations; Image recognition; Iterative algorithms; Neural networks; Testing; Wheels;
Conference_Titel :
Aerospace and Electronics Conference, 1997. NAECON 1997., Proceedings of the IEEE 1997 National
Conference_Location :
Dayton, OH
Print_ISBN :
0-7803-3725-5
DOI :
10.1109/NAECON.1997.622732
