More and more, optimization methods are used to find diverse solution sets. We compare solution diversity in multi-objective optimization, multimodal optimization, and quality diversity in a simple domain. We show that multiobjective optimization does not always produce much diversity, multimodal optimization produces higher fitness solutions, and quality diversity is not sensitive to genetic neutrality and creates the most diverse set of solutions. An autoencoder is used to discover phenotypic features automatically, producing an even more diverse solution set with quality diversity. Finally, we make recommendations about when to use which approach.
翻译：越来越多的是,优化方法被用于寻找多样化的解决方案组。 我们在一个简单领域比较多目标优化、多式优化和质量多样性的解决方案多样性。 我们显示,多目标优化并不总是产生很多多样性,多式优化产生更健康的解决方案,质量多样性对遗传中立性并不敏感,并创造出最多样化的解决方案组。 自动编码器被用来自动发现胎儿特征,产生更多样化的高质量解决方案组。 最后,我们建议何时使用哪种方法。
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