Reachability Analysis for Lexicase Selection via Community Assembly Graphs

TL;DR

This paper introduces community assembly graphs as a novel tool from ecology to analyze the complex search spaces of lexicase selection in evolutionary algorithms, demonstrated on NK landscapes and genetic programming.

Contribution

It proposes a new ecological approach to analyze selection schemes with complex dynamics, extending the analysis capabilities beyond traditional fitness landscapes.

Findings

Community assembly graphs can model lexicase selection dynamics.

The approach is effective on NK landscapes with perfect information.

Successful application to complex genetic programming problems.

Abstract

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.