Evolving phenotypic networks in silico

Evolving Cell Signalling Networks in Silico

The overall goal of the ESIGNET project is to study the computational properties of Cell Signaling Networks (CSN) by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs. In this report we describe the current state of the art in the field. As this project is highly interdisciplinary, we present a the...

Phenotypic Plasticity in Evolving Neural Networks

We present a model based on genetic algorithm and neural networks. The neural networks develop on the basis of an inherited genotype but they show phenotypic plasticity, i.e. they develop in ways that are adapted to the specific environment. The genotype-to-phenotype mapping is not abstractly conceived as taking place in a single instant but is a temporal process that takes a substantial portio...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Evolving networks

Most real networks often evolve through time: changes of topology can occur if some nodes and/or edges appear and/or disappear, and the types or weights of nodes and edges can also change even if the topology stays static. Mobile devices with wireless capabilities (mobile phones, laptops, etc.) are a typical example of evolving networks where nodes or users are spread in the environment and con...

Investigating the Emergence of Phenotypic Plasticity in Evolving Digital Organisms

In the natural world, individual organisms can adapt as their environment changes. In most in silico evolution, however, individual organisms tend to consist of rigid solutions, with all adaptation occurring at the population level. If we are to use artificial evolving systems as a tool in understanding biology or in engineering robust and intelligent systems, however, they should be able to ge...