  Optimization and expansion are two modes of staged evolution of complex
systems where macroscopic observables change at a decreasing, respectively
increasing, rate.
  A prime example of evolutionary expansion, Gross Domestic Product (GDP) time
series gauge economic activities in changing societal structures,} and the
accelerating trend of their growth probably reflects a manyfold increase of the
human interactions that drive change. We show how optimization and expansion
can coexist by replacing `wall clock time' $t$ as independent variable with a
measure of human interactions intensity $\tau$.
  Our analysis of eight centuries of yearly GDP data from three regions of
Western Europe is carried out in two steps. First, a Monte Carlo algorithm is
used to fit the GDP data to a piecewise continuous function comprising a
sequence of exponentials with different exponents.
  In a second step, GDP data are plotted vs. $\tau$ and shown to display two
logarithmic regimes, both decelerating, that are joined by a power-law
cross-over period.
  We connect the end of the first regime and the beginning of the second with
the dawn of the Industrial Revolution and the societal impact of new transport,
communication and production technologies that became widely available after
World War I.
  We conclude that wealth evolution in terms of $\tau$ is a decelerating
process with the hallmarks of record dynamics optimization.
