Dataset for the paper: Temporal true and surrogate fitness landscape analysis for expensive bi-objective optimisation

Fitness landscape analysis can provide valuable insight about key characteristics of a problem. Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes induced by surrogate models is limited, and even non-existent for multi-objective problems. This study addresses this critical gap by comparing landscapes of the real fitness function with those of surrogate models for multi-objective functions. Moreover, it does so temporally by examining landscape features at different points in time during optimisation, in the vicinity of the population at that point in time. We consider the well-known BBOB bi-objective benchmark functions in our experiments and employ a reference-vector guided surrogate-assisted evolutionary algorithm. The results of the landscape analysis on the real fitness landscape reveal significant distinctions between features at different time points during optimisation, and between real and surrogate landscape features. Furthermore, the study demonstrates that both surrogate and real landscape features are of importance when predicting algorithm performance, and that the outcome of an algorithm can be forecast to a decent standard by sampling these during evolution. The results could help to facilitate the design of surrogate switching approaches to improve performance in multi-objective optimisation.  

This dataset contains data for static fitness landscape analyses as well as temporal analyses, focusing on five different surrogates: IDW, IDWR, KNN, LR-KNN, and No-Struct. The dataset comprises six ZIP files in total.

## Folder Descriptions

- idw/: Data related to the temporal analysis using the IDW surrogate.
- idwr/: Data related to the temporal analysis using the IDWR surrogate.
- knn/: Data related to the temporal analysis using the KNN surrogate.
- lr-knn/: Data related the temporal analysis using to the LR-KNN surrogate.
- no-struct/: Data related the temporal analysis using to the No-Struct surrogate.
- static/: Data related to the static analysis.

## Contents per folder

Each analysis is structured into three main folders:

- Samples: Contains all the solutions for true and surrogate fitness landscape feature extraction,  including the performance metric.
- Features: Contains all the true and surrogate features for each repeat of the BBOB-BIOBJ problem.
- Performance: Contains all the performance (hv) for each repeat of the BBOB-BIOBJ problem.