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A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. (arXiv:2204.14061v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
The goal of Quality Diversity Optimization is to generate a collection of
diverse yet high-performing solutions to a given problem at hand. Typical
benchmark problems are, for example, finding a repertoire of robot arm
configurations or a collection of game playing strategies. In this paper, we
propose a set of Quality Diversity Optimization problems that tackle
hyperparameter optimization of machine learning models - a so far underexplored
application of Quality Diversity Optimization. Our benchmark problems involve
novel feature functions, such …
arxiv collection diversity learning machine machine learning machine learning models optimization quality