An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910378474078208 |
|---|---|
| author | Falcón-Cardona, Jesús Guillermo Nezhadmoghaddam, Mahboubeh Bernal-Zubieta, Emilio |
| author_facet | Falcón-Cardona, Jesús Guillermo Nezhadmoghaddam, Mahboubeh Bernal-Zubieta, Emilio |
| contents | The distribution of objective vectors in a Pareto Front Approximation (PFA) is crucial for representing the associated manifold accurately. Distribution Indicators (DIs) assess the distribution of a PFA numerically, utilizing concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or Clustering. Despite the diversity of DIs, their strengths and weaknesses across assessment scenarios are not well-understood. This paper introduces a taxonomy for classifying DIs, followed by a preference analysis of nine DIs, each representing a category in the taxonomy. Experimental results, considering various PFAs under controlled scenarios (loss of coverage, loss of uniformity, pathological distributions), reveal that some DIs can be misleading and need cautious use. Additionally, DIs based on Biodiversity and Potential Energy show promise for PFA evaluation and comparison of Multi-Objective Evolutionary Algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14838 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization Falcón-Cardona, Jesús Guillermo Nezhadmoghaddam, Mahboubeh Bernal-Zubieta, Emilio Neural and Evolutionary Computing The distribution of objective vectors in a Pareto Front Approximation (PFA) is crucial for representing the associated manifold accurately. Distribution Indicators (DIs) assess the distribution of a PFA numerically, utilizing concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or Clustering. Despite the diversity of DIs, their strengths and weaknesses across assessment scenarios are not well-understood. This paper introduces a taxonomy for classifying DIs, followed by a preference analysis of nine DIs, each representing a category in the taxonomy. Experimental results, considering various PFAs under controlled scenarios (loss of coverage, loss of uniformity, pathological distributions), reveal that some DIs can be misleading and need cautious use. Additionally, DIs based on Biodiversity and Potential Energy show promise for PFA evaluation and comparison of Multi-Objective Evolutionary Algorithms. |
| title | An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2403.14838 |