# Random-key genetic algorithms: Principles and applications

arXiv: https://arxiv.org/abs/2506.02120 · HTML: https://mlsanity.com/paper/2506.02120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 122 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Biased Random-Key Genetic Algorithms: A Review](https://mlsanity.com/paper/2312.00961/) (2023)
- [Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms](https://mlsanity.com/paper/2206.03651/) (2022)
- [Early years of Biased Random-Key Genetic Algorithms: A systematic review](https://mlsanity.com/paper/2405.01765/) (2024)
- [Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs](https://mlsanity.com/paper/1611.01210/) (2016)
- [A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks](https://mlsanity.com/paper/2011.11062/) (2020)
- [A biased random-key genetic algorithm for the home health care problem](https://mlsanity.com/paper/2206.14347/) (2022)
- [The double traveling salesman problem with partial last-in-first-out loading constraints](https://mlsanity.com/paper/1908.08494/) (2019)
- [A Non-Dominated Sorting Based Customized Random-Key Genetic Algorithm for the Bi-Objective Traveling Thief Problem](https://mlsanity.com/paper/2002.04303/) (2020)

## Abstract (arXiv)

A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regardless of the problem being addressed. This enhances the productivity and maintainability of the core framework. The algorithm starts with a population of P vectors of random keys. At each iteration, the vectors are partitioned into two sets: a smaller set of high-valued elite solutions and the remaining non-elite solutions. All elite elements are copied, without change, to the next population. A small number of random-key vectors (the mutants) is added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (1991), pairs of solutions. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.