Gene networks have a predictive long-term fitness
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference, Page: 727-734
2013
- 15Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures15
- Readers15
- 15
Conference Paper Description
Using a model of evolved gene regulatory networks, we illustrate several quantitative metrics relating to the long-term evolution of lineages. The k-generation fitness and k-generation survivability measure the evolutionary success of lineages. An entropy measure is used to quantify the predictability of lineage evolution. The metrics are readily applied to any system in which lineage membership can be periodically counted, and provide a quantitative characterization of the genetic landscape, genotype-phenotype map, and fitness landscape. Evolution is shown to be surprisingly predictable in gene networks: only a small number of the possible outcomes are ever observed in multiple replicate experiments. We emphasize the view that the lineage (not the individual, or the genotype) is the evolving entity over the long term. Notably, the long-term fitness is distinct from the short-term fitness. Since evolution is repeatable over the long-term, this implies long-term selection on lineages is possible; the evolutionary process need not be "short-sighted". If we wish to evolve very complex artifacts, it will be expedient to promote the long-term evolution of the genetic architecture by tailoring our models to emphasize long-term selection. Copyright © 2013 ACM.
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