Metaheuristics "In the Large"
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Abstract (arXiv)
Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2020.
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2025Large Language Models for Combinatorial Optimization: A Systematic Reviewcited by 20
- 2024Automated Metaheuristic Algorithm Design with Autoregressive Learningcited by 7
- 2025The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathwayscited by 3
- 2024Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimizationcited by 1
- 2025Large Language Models for Combinatorial Optimization: A Systematic Review4 Jul 2025
- 2025The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways13 Jan 2025
- 2024Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization13 Sep 2024
- 2024Automated Metaheuristic Algorithm Design with Autoregressive Learning6 May 2024