We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this "particularity" approach from the use of lexicase selection in genetic programming to "particularist" approaches to other forms of machine learning and to the design of adaptive systems more generally.

翻译：我们描述了一种自适应系统的设计原则，在该原则下，适应性由环境提出的特定挑战驱动，而非针对众多挑战的平均或聚合性能指标。我们追溯了这种“特性”方法的发展历程，从遗传编程中的lexicase选择，到其他机器学习形式的“特性主义”方法，再到更广泛的自适应系统设计。