# Evolving test instances of the Hamiltonian completion problem

arXiv: https://arxiv.org/abs/2011.02291 · HTML: https://mlsanity.com/paper/2011.02291/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts](https://mlsanity.com/paper/2306.10627/) (2023)
- [Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming](https://mlsanity.com/paper/2403.14146/) (2024)
- [An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models](https://mlsanity.com/paper/2601.12723/) (2026)

## Abstract (arXiv)

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions. That is why recent work proposes a new methodology to generate a diverse set of instances by using an evolutionary algorithm. We can then analyze the resulting graphs and get key insights into which attributes are most related to algorithm performance. We can also fill observed gaps in the instance space in order to generate graphs with previously unseen combinations of features. This methodology is applied to the instance space of the Hamiltonian completion problem using two different solvers, namely the Concorde TSP Solver and a multi-start local search algorithm.