A RNN-Based Hyper-Heuristic for Combinatorial Problems

Designing efficient heuristics is a laborious and tedious task
that generally requires a full understanding and knowledge of a given
optimization problem. Hyper-heuristics have been mainly introduced to
tackle this issue and are mostly relying on Genetic Programming and its
variants. Many attempts in the literature have shown that an automatic
training mechanism for heuristic learning is possible and can challenge
human-based heuristics in terms of gap to optimality. In this work, we
introduce a novel approach based on a recent work on Deep Symbolic
Regression. We demonstrate that scoring functions can be trained using
Recurrent Neural Networks to tackle a well-know combinatorial problem,
i.e., the Multi-dimensional Knapsack. Experiments have been conducted
on instances from the OR-Library and results show that the proposed
modus operandi is an alternative and promising approach to human-
based heuristics and classical heuristic generation approaches.