Hierarchical Surrogate Modeling for Illumination Algorithms
This work addresses a specific bottleneck in evolutionary algorithms for researchers in optimization, but it appears incremental as it builds on existing surrogate modeling techniques.
The paper tackles the challenge of surrogate modeling for evolutionary illumination, which requires representing diverse optimal regions, by proposing a hierarchical decomposition of the training set in feature space to train an ensemble of models, resulting in a method that reduces model complexity.
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.