  In this research, we investigate the possibility of applying a search
strategy to genetic algorithms to explore the entire genetic tree structure.
Several methods aid in performing tree searches; however, simpler algorithms
such as breadth-first, depth-first, and iterative techniques are
computation-heavy and often result in a long execution time. Adversarial
techniques are often the preferred mechanism when performing a probabilistic
search, yielding optimal results more quickly. The problem we are trying to
tackle in this paper is the optimization of neural networks using genetic
algorithms. Genetic algorithms (GA) form a tree of possible states and provide
a mechanism for rewards via the fitness function. Monte Carlo Tree Search
(MCTS) has proven to be an effective tree search strategy given states and
rewards; therefore, we will combine these approaches to optimally search for
the best result generated with genetic algorithms.
