Information Utilization Ratio in Heuristic Optimization Algorithms

TL;DR

This paper introduces the information utilization ratio (IUR), a new metric to quantify how effectively heuristic algorithms use information about the objective function, and demonstrates its relevance to algorithm performance.

Contribution

The paper defines the IUR metric, provides calculation procedures for typical heuristics, and empirically links IUR to heuristic performance, aiding algorithm design and improvement.

Findings

IUR is well-defined and measurable.

Higher IUR correlates with better heuristic performance.

IUR can guide the development of more effective heuristics.

Abstract

Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how finely an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.