  We highlight the flexibility of the IPSO experiment-design framework by
contrasting its application to CMB, weak lensing and redshift surveys. We
illustrate the latter with a 10 parameter MCMC D-optimisation of a dark energy
redshift survey. When averaged over a standard dark energy model space the
resulting optimal survey typically has only one or two redshift bins, located
at z<2. By exploiting optimisation we show how the statistical power of such
surveys is significantly enhanced. Experiment design is aided by the richness
of the figure of merit landscape which means one can impose secondary
optimisation criteria at little cost. For example, one may choose either to
maximally test a single model (such as \Lambda CDM) or to get the most general
model-independent constraints possible (e.g. on a whole space of dark energy
models). Such freedom points to a future where cosmological experiments become
increasingly specialised and optimisation increasingly important.
