# A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization

arXiv: https://arxiv.org/abs/2308.05640 · HTML: https://mlsanity.com/paper/2308.05640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 80 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [A Distributional View on Multi-Objective Policy Optimization](https://mlsanity.com/paper/2005.07513/) (2020)
- [Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models](https://mlsanity.com/paper/1808.00196/) (2018)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [Boxer: Interactive Comparison of Classifier Results](https://mlsanity.com/paper/2004.07964/) (2020)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [The State of the Art in Integrating Machine Learning into Visual Analytics](https://mlsanity.com/paper/1802.07954/) (2018)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment](https://mlsanity.com/paper/2208.04321/) (2022)
- [Diagnosing Concept Drift with Visual Analytics](https://mlsanity.com/paper/2007.14372/) (2020)
- [ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data](https://mlsanity.com/paper/2007.15272/) (2020)
- [VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.13252/) (2021)
- [Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework](https://mlsanity.com/paper/2201.07849/) (2022)
- [A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes](https://mlsanity.com/paper/2009.06876/) (2020)
- [VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization](https://mlsanity.com/paper/2012.01205/) (2020)
- [Towards Better Bus Networks: A Visual Analytics Approach](https://mlsanity.com/paper/2008.10915/) (2020)
- [CNNComparator: Comparative Analytics of Convolutional Neural Networks](https://mlsanity.com/paper/1710.05285/) (2017)
- [DeepTracker: Visualizing the Training Process of Convolutional Neural Networks](https://mlsanity.com/paper/1808.08531/) (2018)
- [Integrating Prior Knowledge in Mixed Initiative Social Network Clustering](https://mlsanity.com/paper/2005.02972/) (2020)
- [How to Read Many-Objective Solution Sets in Parallel Coordinates](https://mlsanity.com/paper/1705.00368/) (2017)
- [It's the Journey Not the Destination: Building Genetic Algorithms Practitioners Can Trust](https://mlsanity.com/paper/2010.06406/) (2020)

## Abstract (arXiv)

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 algorithms. Guided by a literature review and expert interviews, the proposed framework addresses various analytical tasks and establishes a multi-faceted visualization design to support the comparative analysis of intermediate generations in the evolution as well as solution sets. We demonstrate the effectiveness of our framework through case studies on benchmarking and real-world multi-objective optimization problems to elucidate how analysts can leverage our framework to inspect and compare diverse algorithms.