scholarly journals Evolutionary networks from RAD seq loci point to hybrid origins of Medicago carstiensis and Medicago cretacea

2019 ◽  
Vol 106 (9) ◽  
pp. 1219-1228 ◽  
Author(s):  
José Luis Blanco‐Pastor ◽  
Yann J. K. Bertrand ◽  
Isabel María Liberal ◽  
Yanling Wei ◽  
E. Charles Brummer ◽  
...  
Keyword(s):  
Evolutionary Networks

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

scholarly journals Co-evolutionary networks of genes and cellular processes across fungal species

2009 ◽  
Vol 10 (5) ◽  
pp. R48 ◽  
Author(s):  
Tamir Tuller ◽  
Martin Kupiec ◽  
Eytan Ruppin
Keyword(s):  
Fungal Species ◽  
Cellular Processes ◽  
Evolutionary Networks

scholarly journals Coordination and growth: the Stag Hunt game on evolutionary networks

2011 ◽  
Vol 2011 (05) ◽  
pp. P05008 ◽  
Author(s):  
Michele Starnini ◽  
Angel Sánchez ◽  
Julia Poncela ◽  
Yamir Moreno
Keyword(s):  
Stag Hunt ◽  
Stag Hunt Game ◽  
Evolutionary Networks

scholarly journals Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 773 ◽  
Author(s):  
Xueting Wang ◽  
Jun Cheng ◽  
Lei Wang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Behavior Pattern ◽  
Learning Ability ◽  
Predator Prey ◽  
Complex Processes ◽  
Proposed Model ◽  
The Stability ◽  
Reasonable Behavior ◽  
Evolutionary Networks

Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators’ reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator–prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator–prey ecosystem using AI approaches.

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