Sentence examples for evolutionary benchmarks from inspiring English sources

As a result, the community of plant scientists has little sequence data on other plant lineages that could provide insights into common mechanisms of how plants develop and survive in a terrestrial environment, nor do they have any kind of evolutionary benchmarks that might reveal how angiosperms have come to dominate most world ecosystems [ 11].

Housed at the Museo Paleontologico Egidio Feruglio in Trelew, Argentina, the image is an evolutionary benchmark for School of Integrative Plant Science senior research associate Maria A. Gandolfo-Nixon and her postdoctoral associate Nathan Jud as they search for clues to the diversification and biogeography of this plant family.

Also in this case results indicate that QueBN-design can effectively guide the search in very large experimental spaces testing a very limited number of points, outperforming other evolutionary and classical benchmark designs.

For interpretations with regard to the magnitude of the effect, we followed the widely followed benchmarks from evolutionary ecology and other disciplines, in which untransformed r ≈ 0.1 is a small effect, r ≈ 0.3 is a moderate effect, and r ≈ 0.5 is a strong effect (Cohen 1988; Møller and Jennions 2002).

Additionally, it can provide fairly-competitive results compared with other state-of-the-art evolutionary algorithms in both benchmark functions and engineering problems.

DE has been shown to outperform many other evolutionary algorithms on standard benchmark and real-world problems (Vesterstrom and Thomsen 2004).

As a case study, we evaluated the influence of migration related to parameters on the performance of a parallel evolutionary algorithm solving two benchmark problems executed on a multicore processor.

As a case study, we evaluate the influence of migration related to parameters on the performance of the parallel evolutionary algorithm solving two benchmark problems executed on a multicore processor.

While mostly solid, Colorado continues to treat human evolution only implicitly, through broad statements such as, "Organisms are classified into a hierarchy of groups and subgroups based on similarities which reflect their evolutionary relationships" (Standard 3, Benchmark 14 for grades 9 12).

Proposed methodology is studied with four evolutionary optimization algorithms on 25 benchmark functions.

The resulting suite offers 1) an alternative benchmark suite to evolutionary algorithms; 2) a novel method of assessing novel algorithms; and 3) meaningful training and testing problems for evolutionary algorithm selectors and portfolios.