A neural networks for real coded genetic algorithm

This paper proposed an idea and presented a scheme of using neural network to implement real coded genetic algorithm (GA). The arithmetical crossover is generated to its´ multiple parent version and the random mutation operator is also generated to its´ multiple mutation point version, two artificial neuron models are also designed for the two genetic operations. We validate our scheme with some benchmark functions. The significance of our research means that the GA can be implemented with hardware and the inherent parallelism of GA can be explicitly realized, as a result, the real-time performance of GA is remarkably improved, and the application field of GA is widely broadened.