In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any Quality-Diversity (QD) algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%.
The star represents the selected solution to evaluate several times (those reevaluations are shown on the side)
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Unlike the above results, this variant of ARIA has not been initialized with a QD-generated archive.
Instead, it has been initialized with a single individual resulting from an Evolution Strategy optimization.
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