Augmented Neat Algorithm For Enhanced Cognitive Interaction (NEAT-X)

Abstract

Artificial neural networks (ANNs) are utilized in a 
variety of practical applications, from pattern recognition to controlling robots. Neuroevolution (NE), which involves the 
artificial evolution of neural networks through the use of genetic algorithms, has demonstrated significant potential in tackling complicated reinforcement learning tasks. This paper provides a comprehensive overview of the leading methods for evolving artificial neural networks (ANNs), called NeuroEvolution of Augmenting Topologies(NEAT). NEAT excels in evolving neural networks with diverse structures but faces scalability challenges, especially with extensive networks or high-dimensional input spaces. As the complexity of the problem increases, the search space expands exponentially, hindering NEAT's exploration 
effectiveness. After performing mutation, we identify the best mutations, and similar substructures are discovered and added to the mutation list. The improved version of NEAT algorithm requires less computational resources and will give optimized solution. After adding it to the mutation list with some minor modifications, it is demonstrated that the performance of NEAT can be improved.