  In this article the most fundamental decomposition-based optimization method
- block coordinate search, based on the sequential decomposition of problems in
subproblems - and building performance simulation programs are used to reason
about a building design process at micro-urban scale and strategies are defined
to make the search more efficient. Cyclic overlapping block coordinate search
is here considered in its double nature of optimization method and surrogate
model (and metaphore) of a sequential design process. Heuristic indicators apt
to support the design of search structures suited to that method are developed
from building-simulation-assisted computational experiments, aimed to choose
the form and position of a small building in a plot. Those indicators link the
sharing of structure between subspaces ("commonality") to recursive
recombination, measured as freshness of the search wake and novelty of the
search moves. The aim of these indicators is to measure the relative
effectiveness of decomposition-based design moves and create efficient block
searches. Implications of a possible use of these indicators in genetic
algorithms are also highlighted.
