Speeding up the construction of slow adaptive walks

TL;DR

This paper introduces bliss, an algorithm that accelerates the construction of slow adaptive walks by clustering similar strings, reducing computational costs, and enabling efficient exploration of large search spaces in fitness landscapes.

Contribution

The paper presents bliss, a novel clustering-based method that speeds up the creation of slow adaptive walks, improving exploration efficiency in complex search spaces.

Findings

Bliss reduces the quadratic cost of Hamming distance computations.

B-walks constructed from bliss maintain quality in exploring fitness landscapes.

Application of b-walks with Wang-Landau sampling enables larger search space exploration.

Abstract

An algorithm (bliss) is proposed to speed up the construction of slow adaptive walks. Slow adaptive walks are adaptive walks biased towards closer points or smaller move steps. They were previously introduced to explore a search space, e.g. to detect potential local optima or to assess the ruggedness of a fitness landscape. To avoid the quadratic cost of computing Hamming distance (HD) for all-pairs of strings in a set in order to find the set of closest strings for each string, strings are sorted and clustered by bliss such that similar strings are more likely to get paired off for HD computation. To efficiently arrange the strings by similarity, bliss employs the idea of shared non-overlapping position specific subsequences between strings which is inspired by an alignment-free protein sequence comparison algorithm. Tests are performed to evaluate the quality of b-walks, i.e. slow adaptive walks constructed from the output of bliss, on enumerated search spaces. Finally, b-walks are applied to explore larger search spaces with the help of Wang-Landau sampling.