  In the NK model given by Kauffman, myopic local search involves flipping one
randomly-chosen bit of an N-bit decision string in every time step and
accepting the new configuration if that has higher fitness. One issue is that,
this algorithm consumes the full extent of computational resources allocated -
given by the number of alternative configurations inspected - even though
search is expected to terminate the moment there are no neighbors having higher
fitness. Otherwise, the algorithm must compute the fitness of all N neighbors
in every time step, consuming a high amount of resources. In order to get
around this problem, I describe an algorithm that allows search to logically
terminate relatively early, without having to evaluate fitness of all N
neighbors at every time step. I further suggest that when the efficacy of two
algorithms need to be compared head to head, imposing a common limit on the
number of alternatives evaluated - metering - provides the necessary level
field.
