The overall goal of the ESIGNET project is to study the computational properties of cell signalling 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.

As a research area, the field of Evolutionary Algorithms has been under consideration for decades, and a variety of different branches and flavours has been developed. To lay the foundation for the artificial evolution of CSNs, workpackage 5 compares different approaches and identifies several combinations suitable for the project. In this report, a short introduction into the field of Evolutionary Algorithms is given, followed by an outline of an Evolutionary Algorithm capable of evolving artificial CSNs. To improve and speed up the methods, parallel implementations are discussed, followed by a brief section with conclusions for future work.