- Higgins, S.I., Conradi, T. and Muhoko, E., 2023. Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nature Geoscience, 16(2), pp.147-153. [ www ]
- "We further fitted the model using DEoptim which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis), suggesting that the MCMC algorithm performed well compared with the genetic algorithm."
- Storn, R. & Price, K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997).
- Mullen, K., Ardia, D., Gil, D., Windover, D. & Cline, J. DEoptim: an R package for global optimization by differential evolution. J. Stat. Softw. https://doi.org/10.18637/jss.v040.i06 (2011).
- Ardia, D., Boudt, K., Carl, P., Mullen, K. M. & Peterson, B. G. Differential evolution with DEoptim: an application to non-convex portfolio optimization. R J. 3, 27–34 (2011).
- "We further fitted the model using DEoptim which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis), suggesting that the MCMC algorithm performed well compared with the genetic algorithm."