A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization
Yansong Huang, Zherui Zhang, Ao Jiao, Yuxin Ma, Ran Cheng

TL;DR
This paper introduces a visual analytics framework that enhances the comparison and understanding of evolutionary processes in multi-objective optimization algorithms through interactive visualization tools.
Contribution
The paper presents a novel visual analytics framework for detailed analysis and comparison of EMO algorithms' internal processes, addressing a gap in existing black-box approaches.
Findings
Framework supports exploration of intermediate generations
Case studies demonstrate improved algorithm comparison
Enables detailed inspection of evolutionary dynamics
Abstract
Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems. In real-world applications, analysts often employ several algorithms concurrently and compare their solution sets to gain insight into the characteristics of different algorithms and explore a broader range of feasible solutions. However, EMO algorithms are typically treated as black boxes, leading to difficulties in performing detailed analysis and comparisons between the internal evolutionary processes. Inspired by the successful application of visual analytics tools in explainable AI, we argue that interactive visualization can significantly enhance the comparative analysis between multiple EMO algorithms. In this paper, we present a visual analytics framework that enables the exploration and comparison of evolutionary processes in EMO…
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Taxonomy
TopicsDesign Education and Practice
MethodsVisual Analytics
