Your English writing platform
Discover LudwigExact(60)
For example, neutral networks, though powerful, lack explainability.
Random graph theory is used to construct networks in sequence space which are suitable models for neutral networks.
Here we propose a new technique to quickly jump out of neutral networks and to reach better fitness regions.
Some activities may have been isolated in sequence space, but others could have been approached along large, interconnected neutral networks.
The pre-images of this map, called neutral networks, are uniquely associated with the shapes and vice versa.
The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN).
Our results suggest that the highland corresponds to a phase-transition threshold of the formation of the nearly neutral networks.
Customary hill-climbing optimizing paradigms turn out to be unsuitable to walk and search such large neutral networks.
Replication and mutation on neutral networks are modeled by phenomenological rate equations as well as by a stochastic birth-and-death model.
This function is designed for understanding the performance of evolutionary algorithms on real world problems in which many "neutral networks" are assumed to be found.
Surprisingly, the magnitude of observed variation in evolvability can neither be explained by differences in the size nor the topology of the neutral networks.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com