What question did this study set out to answer?

This work aims to improve the effectiveness and interpretability of optimization algorithms by addressing their structural awareness and transparency limitations.

April 15, 2026

Improving Optimization Algorithms with Machine Learning and Visualization

Key Points

This work aims to improve the effectiveness and interpretability of optimization algorithms by addressing their structural awareness and transparency limitations.
Investigative analysis of existing optimization algorithms
Integration of machine learning techniques
Development of visualization tools for improved interpretability
Identification of common shortcomings in current optimization algorithms
Demonstration of enhanced performance with machine learning integration
Improved user understanding of algorithm behavior through visualization tools

Abstract

This dissertation explores how optimization algorithms can become both more effective and more interpretable. It focuses on two long-standing limitations of metaheuristics: first, their frequent lack of awareness of the specific structure of the problem they are solving; and second, their opacity, which makes it difficult to understand why they behave well in some cases and poorly in others.

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Authors

Camilo Chacón Sartori

Journals

ACM SIGEVOlution

Actions

Institutions

Artificial Intelligence Research Institute

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Improving Optimization Algorithms with Machine Learning and Visualization

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study