<ul><li>In real-world applications, users often favor structurally diverse design choices over one high-quality solution.</li><li>This paper aims to identify a fixed number of solutions with a specified distance while maximizing their average quality.</li><li>An empirical study suggests that uniform random sampling performs well in producing diverse high-quality solutions.</li><li>The study emphasizes the need for algorithms tailored to produce diverse solutions of high average quality.</li></ul>

Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization

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