What can we learn from slow self-avoiding adaptive walks by an infinite radius search algorithm?

TL;DR

This paper analyzes slow self-avoiding adaptive walks generated by an infinite radius search algorithm across various problems, revealing insights into landscape structure, difficulty, and local optima detection.

Contribution

It introduces the concept of node viscidity and hierarchical walks to differentiate landscape ruggedness and hierarchy, advancing understanding of search difficulty and landscape structure.

Findings

Slacker walks can indicate problem difficulty and landscape structure.

Hierarchical walks differentiate between hierarchical and anarchic landscapes.

Node viscidity is a promising measure for local optima potential.

Abstract

Slow self-avoiding adaptive walks by an infinite radius search algorithm (Limax) are analyzed as themselves, and as the network they form. The study is conducted on several NK problems and two HIFF problems. We find that examination of such "slacker" walks and networks can indicate relative search difficulty within a family of problems, help identify potential local optima, and detect presence of structure in fitness landscapes. Hierarchical walks are used to differentiate rugged landscapes which are hierarchical (e.g. HIFF) from those which are anarchic (e.g. NK). The notion of node viscidity as a measure of local optimum potential is introduced and found quite successful although more work needs to be done to improve its accuracy on problems with larger K.