An Algorithm to Effect Prompt Termination of Myopic Local Search on Kauffman-s NK Landscape

TL;DR

This paper introduces an algorithm for Kauffman’s NK landscape that efficiently terminates myopic local search early, reducing unnecessary fitness evaluations and enabling fair comparisons between algorithms.

Contribution

The paper proposes a novel algorithm that allows early logical termination of local search in NK landscapes, saving computational resources and facilitating fair algorithm comparisons.

Findings

The algorithm reduces the number of fitness evaluations needed.

Early termination does not compromise search effectiveness.

Fair comparison is enabled through metering of evaluations.

Abstract

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.