Human-derived heuristic enhancement of an evolutionary algorithm for the 2d bin-packing problem

Abstract

The 2D Bin-Packing Problem (2DBPP) is an NP-Hard combinatorial optimisation problem with many real-world analogues. Fully deterministic methods such as the well-known Best Fit and First Fit heuristics, stochastic methods such as Evolutionary Algorithms (EAs), and hybrid EAs that combine the deterministic and stochastic approaches have all been applied to the problem. Combining derived human expertise with a hybrid EA offers another potential approach. In this work, the moves of humans playing a gamified version of the 2DBPP were recorded and four different Human-Derived Heuristics (HDHs) were created by learning the underlying heuristics employed by those players. Each HDH used a decision tree in place of the mutation operator in the EA. To test their effectiveness, these were compared against hybrid EAs utilising Best Fit or First Fit heuristics as well as a standard EA using a random swap mutation modified with a Next Fit heuristic if the mutation was infeasible. The HDHs were shown to outperform the standard EA and were faster to converge than – but ultimately outperformed by – the First Fit and Best Fit heuristics. This shows that humans can create competitive heuristics through gameplay and helps to understand the role that heuristics can play in stochastic search.