It is a form of artificial intelligence that is designed to reach a specified output or determined result based on predetermined factors. The genetic algorithms are a form of Darwin's theory of survival of the fittest. By using the main components of each the author illustrates how the two can be used to develop pattern recognition in the hand writing example given. The program is designed to recognize patterns that resemble close patterns and not a specific given pattern. Similarly, if the program contains too few neurons the program will not be able to recognize the pattern either.
Information is encoded into the chromosomes, which are four bits.
The fitness function will be executed to yield a population that will best represent a fit for the neural network. By limiting the program to have 15 neurons, the genetic algorithm will be able to make only 15 crossovers to find the best possible match for the pattern recognition. The neural network will train for 500 cycles but use the last 40 cycles for training purposes. The fitness function will be calculated based on the learning error, the number of input neurons that are ignored, and the number of hidden layers of neurons. Further discussion can be made about this material, but reference to book and the program will be needed.