# Hierarchical Surrogate Modeling for Illumination Algorithms

## TL;DR

This paper introduces a hierarchical surrogate modeling approach for evolutionary illumination algorithms, enabling efficient representation of diverse optimal regions by decomposing training data and using ensemble models.

## Contribution

It proposes a novel hierarchical segmentation method to improve surrogate modeling in illumination algorithms, addressing the challenge of representing many diverse optimal regions.

## Key findings

- Enhanced surrogate model accuracy for diverse solutions
- Reduced computational complexity through hierarchical segmentation
- Improved performance in illumination tasks

## Abstract

Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.