Asymmetric evolving random networks

A simple asymmetric evolving random network

We introduce a new oriented evolving graph model inspired by biological networks. A node is added at each time step and is connected to the rest of the graph by random oriented edges emerging from older nodes. This leads to a statistical asymmetry between incoming and outgoing edges. We show that the model exhibits a percolation transition and discuss its universality. Below the threshold, the ...

Brainwashing Random Asymmetric `Neural' Networks

An algorithm for synaptic modiication (plasticity) is described by which a recurrently connected network of neuron-like units can organize itself to produce a sequence of activation states that does not repeat itself for a very long time. During the self-organization stage, the connections between the units undergo non-Hebbian modiications, which tend to decorrelate the activity of the units, t...

Evolving populations of random boolean networks

We investigate the adaptation of Random Boolean Networks that are a model for regulatory gene networks. The model considers a general genetic algorithm and a fitness function that takes into account the full network dynamical behavior.Our simulations shows a fast decrease on algorithm perfomance when we consider larger networks or networks with more complex dynamical behavior. Finally, we discu...

Resolving social dilemmas on evolving random networks

We show that strategy-independent adaptations of random interaction networks can induce powerful mechanisms, ranging from the Red Queen to group selection, which promote cooperation in evolutionary social dilemmas. These two mechanisms emerge spontaneously as dynamical processes due to deletions and additions of links, which are performed whenever players adopt new strategies and after a certai...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation