| Abstract: |  Continuous global blackbox optimization is one of the important problems in
today’s science and engineering routine. Although there exist many algorithms,
which are able to solve the problem, all of them is applicable only on relatively
constrained class of optimized functions.
The present tendency is to look for the algorithm, which could solve larger class
of problems - preferably larger, than all other algorithms - and which would work
for all funcions in this class with acceptable power.
I am focusing on not yet well-explored way of the optimization in this work. The
Neural Gas - despite it falls into another algorithm class - looks like to be acceptable
for being used as a global optimizer after some changes thanks to its properties.
After a brief introduction, I present two ways of how the Neural Gas can be used
for optimization, found in the literature.
Next I try to reproduce the authors’ reached results, which they present in their
articles.
In the last part of the work I compare the algorithms with each other to see
which is better and their advantages and disadvantages.
During the work the becomes obvious, that the authors of both articles evidently
did some mistakes during writing them, because not even one of the algorithms works
as it should. This made me often deep in thought about trustfulness of technical
articles.
Nevertheless the Neural Gas showed that can be quite good, compared with
other algorithms, in the future.
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