Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from a given starting point. However, simple fitness landscapes are inappropriate for analyzing the search space seen by selection schemes like lexicase selection in which the outcome of selection depends heavily on the current contents of the population (i.e. selection schemes with complex ecological dynamics). Here, we propose borrowing a tool from ecology to solve this problem: community assembly graphs. We demonstrate a simple proof-of-concept for this approach on an NK Landscape where we have perfect information. We then demonstrate that this approach can be successfully applied to a complex genetic programming problem. While further research is necessary to understand how to best use this tool, we believe it will be a valuable addition to our toolkit and facilitate analyses that were previously impossible.

翻译：适应度景观历来是分析进化算法探索搜索空间的强大工具。特别是，它们有助于理解从给定起点到达最优解的难易程度。然而，简单的适应度景观不适用于分析诸如语言选择（lexicase selection）这类选择方案所见的搜索空间，此类选择方案的结果严重依赖于群体当前的组成（即具有复杂生态动态的选择方案）。在此，我们提出借用生态学中的工具来解决这一问题：社区组装图。我们在具有完全信息的NK景观上展示了该方法的简单概念验证。随后，我们证明该方法可成功应用于复杂的遗传编程问题。尽管仍需进一步研究以理解如何最佳使用这一工具，但我们相信它将为我们的工具箱增添宝贵价值，并促进以往不可能实现的分析。