An Algorithm to Effect Prompt Termination of Myopic Local Search on Kauffman-s NK Landscape
This work addresses a computational efficiency problem for researchers using the NK model, but it appears incremental as it modifies an existing search method without demonstrating broad impact.
The paper tackles the inefficiency of myopic local search on the Kauffman NK landscape, where the algorithm unnecessarily evaluates all neighbors at each step, and proposes an algorithm that enables early termination without full neighbor evaluations, though no concrete performance numbers are provided.
In Kauffman-s NK model, 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.