  Computational design is aimed at supporting or automating design processes
using computational techniques. However, some classes of design tasks involve
criteria that are difficult to handle only with computers. For example, visual
design tasks seeking to fulfill aesthetic goals are difficult to handle purely
with computers. One promising approach is to leverage human computation; that
is, to incorporate human input into the computation process. Crowdsourcing
platforms provide a convenient way to integrate such human computation into a
working system.
  In this chapter, we discuss such computational design with crowds in the
domain of parameter tweaking tasks in visual design. Parameter tweaking is
often performed to maximize the aesthetic quality of designed objects.
Computational design powered by crowds can solve this maximization problem by
leveraging human computation. We discuss the opportunities and challenges of
computational design with crowds with two illustrative examples: (1) estimating
the objective function (specifically, preference learning from crowds' pairwise
comparisons) to facilitate interactive design exploration by a designer and (2)
directly searching for the optimal parameter setting that maximizes the
objective function (specifically, crowds-in-the-loop Bayesian optimization).
