Editing Pareto efficiency (section)

== Use in biology ==
Pareto optimisation has also been studied in biological processes.<ref>Moore, J. H., Hill, D. P., Sulovari, A., & Kidd, L. C., "Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing", in R. Riolo, E. Vladislavleva, M. D. Ritchie, & J. H. Moore (eds.), ''Genetic Programming Theory and Practice X'' (Berlin/Heidelberg: Springer, 2013), [https://books.google.com/books?id=YZZAAAAAQBAJ&pg=PA86 pp.&nbsp;87–102].</ref> In bacteria, [[Gene|genes]] were shown to be either inexpensive to make (resource-efficient) or easier to read ([[Translation (biology)|translation]]-efficient). [[Natural selection]] acts to push highly expressed genes towards the Pareto frontier for resource use and translational efficiency.<ref>Eiben, A. E., & Smith, J. E., ''Introduction to Evolutionary Computing'' (Berlin/Heidelberg: Springer, 2003), [https://books.google.com/books?id=7IOE5VIpFpwC&pg=PT166 pp. 166–169].</ref> Genes near the Pareto frontier were also shown to evolve more slowly (indicating that they are providing a selective advantage).<ref>Seward, E. A., & Kelly, S., [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1480-7 "Selection-driven cost-efficiency optimization of transcripts modulates gene evolutionary rate in bacteria"], ''[[Genome Biology]]'', Vol.&nbsp;19, 2018.</ref>