Instance Space Analysis of Combinatorial Multi-objective Optimization Problems

In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space an...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2020 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8
Hlavní autoři: Yap, Estefania, Munoz, Mario A., Smith-Miles, Kate, Liefooghe, Arnaud
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.07.2020
Témata:
black-box combinatorial optimization
Clustering algorithms
Correlation
Decision trees
Feature extraction
feature-based performance prediction
landscape analysis
Multi-objective optimization
Optimization
Prediction algorithms
Visualization
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performance and understanding strengths and weaknesses is investigated. Furthermore, we explore the impact of various definitions of "good" performance. The visualization of the instance space provides an alternative method of algorithm discrimination by showing clusters of instances where algorithms perform well across the instance space. We validate the suitability of existing features and identify opportunities for future development.
DOI:10.1109/CEC48606.2020.9185664