A Review Study on Designing Neural Networks Using Genetic Algorithms through the Intelligence System
The whole world is watching closely the advancements in the fields of Neural Networks and Genetic Algorithms. The interest among scientists, statisticians, and engineers related to these fields is indeed too much. These two highly sophisticated machine learning techniques have gained high popularity only in recent times. This paper takes an in-depth review of various scientific research works performed so far on the optimization of neural networks with the help of the genetic algorithm search process. Neural networks have been thought to have several limitations for which their effectiveness in solving complicated and apparently challenging issues is often questioned. Optimization of neural networks is an aim to deviate from that common notion. We have undertaken detailed analysis and discussion of the research works published so far related to neural network types, the application domain of neural network, neural network designing using genetic algorithm, and optimal values of genetic algorithm operators on the basis of the size of the population, mutation rate, and crossover rate. This research review-based study will help both novice and experienced researchers to choose the right categories of genetic algorithm operators for creating need-based neural networks. At the same time, the scope of future research in this field will also be unveiled.