Then…, I realized what I involve with is something out ordinary. This is about my thesis.
When this topic was given to me, I thought it might be easy. It just about geometry optimization and I would have done it soon. In fact, this is not quite easy as I thought. I face a very complex task. I must define aerodynamic characteristic by ground effect influence. The problem is the phenomenon of ground effect is barely explained by any theories nor even empirical formulae. For this time being the phenomenon just depicts the tendency of aerodynamic characteristic alteration. That is one problem.
Another problem is about optimization itself. I decided to use genetic algorithms for my task since I play in stochastic or random data. Genetic algorithms are a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). [http://en.wikipedia.org/]
So, what I should do? Keep working on it, obviously. One thing I grateful for this, I can enrich myself with the universe of knowledge. Something hardly resistible, is it?
When this topic was given to me, I thought it might be easy. It just about geometry optimization and I would have done it soon. In fact, this is not quite easy as I thought. I face a very complex task. I must define aerodynamic characteristic by ground effect influence. The problem is the phenomenon of ground effect is barely explained by any theories nor even empirical formulae. For this time being the phenomenon just depicts the tendency of aerodynamic characteristic alteration. That is one problem.
Another problem is about optimization itself. I decided to use genetic algorithms for my task since I play in stochastic or random data. Genetic algorithms are a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). [http://en.wikipedia.org/]
So, what I should do? Keep working on it, obviously. One thing I grateful for this, I can enrich myself with the universe of knowledge. Something hardly resistible, is it?