mlsanityData to 14 Oct 2026

The Vision of Self-Evolving Computing Systems

arXiv 2204.06825 ↗First posted 14 Apr 2022cs.SE cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing system detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 100 references that are in the map. First posted on arXiv 14 Apr 2022.

Builds on

11 of 100 references.

References · 11

  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  2. 2016FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Softwarecited by 18
  3. 2016A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systemscited by 18
  4. 2017Provable Self-Representation Based Outlier Detection in a Union of Subspacescited by 18
  5. 2017Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Casescited by 14
  6. 2021Software Architecture for ML-based Systems: What Exists and What Lies Aheadcited by 13
  7. 2019Perpetual Assurances for Self-Adaptive Systemscited by 9
  8. 2020Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guidelinecited by 9
  9. 2016Dynamic Multi-Objectives Optimization with a Changing Number of Objectivescited by 8
  10. 2019ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systemscited by 5
  11. 2021Lifelong Computingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.

4 papers.