  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.
