Metaheuristics "In the Large"
Neural and Evolutionary Computing
2021-08-31 v4 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2011.09821,
title = {Metaheuristics "In the Large"},
author = {Jerry Swan and Steven Adriaensen and Alexander E. I. Brownlee and Kevin Hammond and Colin G. Johnson and Ahmed Kheiri and Faustyna Krawiec and J. J. Merelo and Leandro L. Minku and Ender Özcan and Gisele L. Pappa and Pablo García-Sánchez and Kenneth Sörensen and Stefan Voß and Markus Wagner and David R. White},
journal= {arXiv preprint arXiv:2011.09821},
year = {2021}
}