Case Study: An Analysis Of Accidental Complexity In A State-Of-The-Art Hyper-Heuristic For Hyflex

While simplicity is an important factor affecting algorithm re-usability, it is often overlooked in algorithm design, which has a tendency to produce overly complex methods. In this paper we demonstrate Accidental Complexity Analysis (ACA), a research practice targeted at detecting and eliminating accidental complexity, without loss of performance (c.f. refactoring in software engineering), using it to analyze the presence of accidental complexity in GIHH, a state-of-the-art selection hyper-heuristic for HyFlex. We identify various algorithmic sub-mechanisms contributing little to GIHH's overall performance, and validate many other. As an outcome we present Lean-GIHH, a simplified, re-implementation of GIHH.