  Under high complexity - given by pervasive interdependence between
constituent elements of a decision in an NK landscape - our algorithm obtains
fitness superior to that reported in extant research. We distribute the
decision elements comprising a decision into clusters. When a change in value
of a decision element is considered, a forward move is made if the aggregate
fitness of the cluster members residing alongside the decision element is
higher. The decision configuration with the highest fitness in the path is
selected. Increasing the number of clusters obtains even higher fitness.
Further, implementing moves comprising of up to two changes in a cluster also
obtains higher fitness. Our algorithm obtains superior outcomes by enabling
more extensive search, allowing inspection of more distant configurations. We
name this algorithm the muddling through algorithm, in memory of Charles
Lindblom who spotted the efficacy of the process long before sophisticated
computer simulations came into being.
