# Novelty Search in Competitive Coevolution

arXiv: https://arxiv.org/abs/1407.0576 · HTML: https://mlsanity.com/paper/1407.0576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2014.

## Built on by (most cited first, 2 of 2)

- [DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence](https://mlsanity.com/paper/2312.05171/) (2023)
- [Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback](https://mlsanity.com/paper/2505.13562/) (2025)

## Abstract (arXiv)

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.