Modeling and tuning genetic algorithms

Here is our latest work that presents a solution on the problem of Service Chain Embedding. It is based on genetic algorithms and extends a previously published conference paper.

The interesting contribution of this paper is a modeling framework for the operation of genetic algorithms. Using this framework we prove that NP-hard problems are not computed efficiently by genetic algorithms and we define some properties for the problems that genetic algorithms compute efficiently. 

Another interesting contribution of this paper is a performance optimization mechanism for genetic algorithms which is also based on genetic computing. So you use one genetic algorithm in order to optimize the performance of another.

https://doi.org/10.1007/s10922-023-09771-y

A thesis on virtual network embedding with genetic algorithms

Recently I finished my master thesis which is a study on the problem of virtual network embedding (VNE). It concerns the virtualization of network resources and topologies so as to create a fully functional virtual network embedded in a detacenter, instead of using a physical stand alone network. There are many advantages in this approach as it is much easier to update and maintain virtual devices and links instead of physical ones.

There are many ways to map virtual on physical resources, a relatively small virtual topology may be embedded in many ways in the physical network of a datacenter. The problem of VNE concerns the finding of the ideal mapping so that virtualized network will have the optimal performance.

It is a hard problem to solve so traditional analytical methods are inefficient for its solution. Artificial intelligence methods provide in such cases good and efficient solutions. In the thesis I developed a genetic algorithm that approaches the problem as an optimization problem and provides good practical solutions. 

My thesis is written in Greek and is accessible in the repository of the Hellenic Open University.  A direct download of the paper is possible in this link.