Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network

Abstract:

In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1785-1789
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1785
• Online since: June 2012
• Authors: Tai Shan Yan
• Keywords: BP Algorithm, Evolutionary Neural Network, Genetic Algorithm (GA), Human Reproduction Mode, Motor Fault Diagnosis

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