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.

翻译：本研究探究了将搜索策略应用于遗传算法以探索完整遗传树结构的可能性。多种方法有助于执行树搜索，但广度优先、深度优先及迭代等简单算法计算量巨大，常导致执行时间过长。对抗性技术在概率搜索中通常更受青睐，能够更快地获得最优结果。本文试图解决的问题是利用遗传算法优化神经网络。遗传算法（GA）构建了一个可能状态树，并通过适应度函数提供奖励机制。蒙特卡洛树搜索（MCTS）已被证明是一种在给定状态和奖励条件下的有效树搜索策略。因此，我们将结合这两种方法，以最优方式搜索遗传算法生成的最佳结果。