Local search heuristics: Fitness Cloud versus Fitness Landscape

TL;DR

This paper proposes the fitness cloud as a novel visualization tool for search spaces, offering advantages over traditional fitness landscapes, and analyzes local search heuristics on NK landscapes using this concept.

Contribution

It introduces the fitness cloud as an alternative to fitness landscapes and demonstrates its effectiveness in analyzing local search heuristics on NK landscapes.

Findings

Fitness cloud overcomes deficiencies of fitness landscape visualization.

Fitness vs. fitness correlation relates to the epistatic parameter K.

Analysis of local search heuristics behavior on NK landscapes.

Abstract

This paper introduces the concept of fitness cloud as an alternative way to visualize and analyze search spaces than given by the geographic notion of fitness landscape. It is argued that the fitness cloud concept overcomes several deficiencies of the landscape representation. Our analysis is based on the correlation between fitness of solutions and fitnesses of nearest solutions according to some neighboring. We focus on the behavior of local search heuristics, such as hill climber, on the well-known NK fitness landscape. In both cases the fitness vs. fitness correlation is shown to be related to the epistatic parameter K.