An Approach to Analyze Niche Evolution in XCS Models
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916661772156928 |
|---|---|
| author | Lanzi, Pier Luca |
| author_facet | Lanzi, Pier Luca |
| contents | We present an approach to identify and track the evolution of niches in XCS that can be applied to any XCS model and any problem. It exploits the underlying principles of the evolutionary component of XCS, and therefore, it is independent of the representation used. It also employs information already available in XCS and thus requires minimal modifications to an existing XCS implementation. We present experiments on binary single-step and multi-step problems involving non-overlapping and highly overlapping solutions. We show that our approach can identify and evaluate the number of niches in the population; it also show that it can be used to identify the composition of active niches to as to track their evolution over time, allowing for a more in-depth analysis of XCS behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Approach to Analyze Niche Evolution in XCS Models Lanzi, Pier Luca Neural and Evolutionary Computing Machine Learning We present an approach to identify and track the evolution of niches in XCS that can be applied to any XCS model and any problem. It exploits the underlying principles of the evolutionary component of XCS, and therefore, it is independent of the representation used. It also employs information already available in XCS and thus requires minimal modifications to an existing XCS implementation. We present experiments on binary single-step and multi-step problems involving non-overlapping and highly overlapping solutions. We show that our approach can identify and evaluate the number of niches in the population; it also show that it can be used to identify the composition of active niches to as to track their evolution over time, allowing for a more in-depth analysis of XCS behavior. |
| title | An Approach to Analyze Niche Evolution in XCS Models |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2503.18961 |