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

Distributed Unsupervised Deep Learning

Our recently published paper, available here in open access mode, presents a deep learning method for network resource orchestration. There are a few features that make this method interesting.

It is build on a distributed multi-agent architecture.

It is based on Unsupervised Deep Learning, not a common feature for resource orchestration methods. The user essentially defines an objective and the agents try to accomplish it by training and then running deep neural networks and without further interaction with the user.

The agents share among them the most efficient models making the training process more efficient.

The neural networks are trained using genetic algorithms which is an innovative feature for unsupervised learning systems and speeds up the training procedure. It is actually interesting to use one system in order to train another system without explicitly describing the training process.

We are able to test this method by running simulations in large scale topologies. For this we have built an efficient network simulator resealed as an open source project.

https://doi.org/10.1109/ACCESS.2023.3308492

 

An application for reading BIG files

There are many times that I want to read a big text file but current text viewers cannot handle it. 

So I decided to develop an application that would put me out of this trouble. 

It is open source and reseased in this repository

https://github.com/rodispantelis/tools

On the Creative AI rush

It is weird how people react these days about Creative AI, especially ChatGPT. It was released a few months ago and many people already have developed an addiction on it while many others hate it or are afraid of it passionately.

How can serious professionals depend on a chat bot on their daily work? From my perspective they are either not serious or not professionals. Using such a great tool is reasonable but suddenly to depend on it out of the blue is  not rational. When the service stopped a few days ago some people were panicked. Really???

It is actually a great tool and has many programming potentials. The statement that these capabilities will some day signal the end of programming is not accurate. Trivial programming has passed away long ago. There are many tools and websites that actually help you build applications in seconds, it is not only chatGPT. But really innovative programming cannot be created by such chat bots that only use the knowledge that they have from their training and cannot, at least yet, create knowledge and science.

About the fear on the use of AI. Well, as any other technological achievement it can be used for the good or the bad, for the best or the worst of humanity. You can use a knife to cook or kill, on the same spirit you can use AI to detect and treat cancer or manipulate the elections.

About chatGPT

These days the talk of the town is ChatGPT and its potential uses. It really is a great tool and it impressed me, and everybody else, with the way it responds to simple language questions.
Of course it has some flaws. There is some criticism about its math capabilities or the accuracy of some information it provides, like historical dates and paper citations. But this doesn't really bother me; if I wanted math calculations I would use a calculator and for dates and historical events I would prefer Wikipedia. There are more fundamental concerns about the use of chatGPT, especially about its potential use in research as many people claim (or fear) that generative AI could replace original research. Well, not yet.
The current model of chatGPT doesn't have critical "thinking" and has troubles on reasoning and induction. I asked it some questions about how certain AI methods could be combined on an innovative way, like genetic algorithms and unsupervised learning. As there are no references of such combinations, chatGPT discouraged this idea. I would expect an answer that would reason for or against this idea based on the features of these two methods.
Then I thought of asking a stupid and easy question, but it still failed. The question was why my favourite football team cannot win Champions League. The obvious question is that it is a weak team and cannot face the competition. But the model responded that it's just a language model and cannot answer. I had to guide it through some more questions to make it answer correctly.
The cause for these weaknesses must be the architecture of the model. Probably more interconnections among the layers of the model will improve its ability to reason and combine previous knowledge. But this is more of future goal, currently it cannot replace original research and we should probably cross check its quite interesting answers.