# Using coevolution and substitution of the fittest for health and well-being recommender systems

arXiv: https://arxiv.org/abs/2211.00414 · HTML: https://mlsanity.com/paper/2211.00414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Long-Term Progress and Behavior Complexification in Competitive Co-Evolution](https://mlsanity.com/paper/1909.08303/) (2019)
- [Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms](https://mlsanity.com/paper/2108.03156/) (2021)

## Abstract (arXiv)

This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.