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 for reasoning about a building design process at micro-urban scale and defining strategies for making the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphor) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are then developed from buildingsimulation- 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 subproblems (“commonality”) to recursive recombination, measured as freshness of the search wake and novelty of the search moves, and can be of assistance in devising search structures suitable for being search efficiently. This is because they bring some memory of the search history to algorithms which otherwise would have not one. The aim of these indicators is to measure the relative effectiveness of alternatives for recursively decomposing problems so as to make searches more efficient than randomly structured ones. Implications of a possible use of these indicators in genetic algorithms are also highlighted.
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