Genetic Network Programming (GNP) is an evolutionary algorithm that extends Genetic Programming (GP). It is typically used in agent control problems. In contrast to GP, which employs a tree structure, GNP utilizes a directed graph structure. During the evolutionary process, the connections between nodes change to discover the optimal strategy. Due to the large number of node connections, GNP has a large search space, making it challenging to identify an appropriate graph structure. One way to reduce this search space is by utilizing simplified operators that restrict the changeable node connections to those participating in the fitness function. However, this method has not been applied to GNP structures that use separate graphs for each agent, such as situation-based GNP (SBGNP). This paper proposes a method to apply simplified operators to SBGNP. To evaluate the performance of this method, we tested it on the Tileworld benchmark, where the algorithm demonstrated improvements in average fitness.

翻译：遗传网络规划（GNP）是一种扩展了遗传规划（GP）的进化算法，通常应用于智能体控制问题。与采用树状结构的GP不同，GNP采用有向图结构。在进化过程中，节点间的连接会发生改变以发现最优策略。由于节点连接数量庞大，GNP的搜索空间很大，这使得寻找合适的图结构具有挑战性。一种缩减该搜索空间的方法是使用简化算子，将可变的节点连接限制在参与适应度函数计算的连接范围内。然而，该方法尚未应用于为每个智能体使用独立图结构的GNP变体，例如基于情境的GNP（SBGNP）。本文提出了一种将简化算子应用于SBGNP的方法。为评估该方法的性能，我们在Tileworld基准测试中进行了实验，结果表明该算法在平均适应度上取得了提升。