# Fitness Approximation through Machine Learning

arXiv: https://arxiv.org/abs/2309.03318 · HTML: https://mlsanity.com/paper/2309.03318/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 71 references that are in the map.
- First posted on arXiv 6 Sep 2023.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding](https://mlsanity.com/paper/2112.03534/) (2021)
- [Evolving Context-Aware Recommender Systems With Users in Mind](https://mlsanity.com/paper/2007.15409/) (2020)
- [EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration](https://mlsanity.com/paper/2207.10367/) (2022)
- [Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problem](https://mlsanity.com/paper/1605.06722/) (2016)

## Abstract (arXiv)

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update a fitness-approximation ML model throughout an evolutionary run. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA -- depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly -- our approach is generic and can be easily applied to many different domains.