On the Fitness Landscapes of Interdependency Models in the Travelling Thief Problem

TL;DR

This paper investigates how various dependency structures in the Travelling Thief Problem affect the fitness landscape, using Local Optima Networks to analyze the problem's complexity with a simple local search.

Contribution

It introduces an analysis of different dependency forms in TTP and applies Local Optima Networks to understand their impact on the problem's landscape.

Findings

Different dependency structures significantly alter the fitness landscape.

Local Optima Networks reveal varying landscape ruggedness based on dependency types.

Insights can guide the design of more effective heuristics for TTP.

Abstract

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight of the knapsack. However, other forms of dependency as well as combinations of dependencies should be considered for investigation, as they are often found in complex real-world problems. Our goal is to study the impact of different forms of dependency in the TTP using a simple local search algorithm. To achieve this, we use Local Optima Networks, a technique for analysing the fitness landscape.