Changing Phd topic in between because of lack of 'usefulness' of current research In my current study. I am trying to solve a problem A by using a technology B. My supervisors consider themselves as experts in B. I have a scholarship for 3 years and 1.5 years are over. I have passed my exams and my Ph.D. proposal presentation was passed 6 months back.  But now I realize that B=evolutionary computation is not a useful technique and is incapable of solving any real problems in the industry. I have a first-hand experience with that, now.

I wish to explore C and D as techniques but my supervisor is totally against it stating he cannot help me in that. Considering I am using a scholarship I am under additional pressure to do only what his 'vision' states. Any other technique I propose is challenged vociferously by them even in the inception stage. 

What should I do? I can confront them and say that EC is useless and I wish to explore techniques which actually do something. There is no top conference which will accept my work but it lead to extreme pressure and  argument. My supervisor is only interested in his growth within the academic community of EC and has no concern about what industry does. I do not wish to be an academician and investing my time on training myself in such a work is not in sync with my aspirations. 

Kindly suggest as to what I should do? Should I give up the scholarship and look for other Ph.D. opportunities? That is a huge waste of time and effort, though. The scholarship is given by university and not through my supervisor's funding.

What is a good way out here?
First of all: talk to your supervisor! what does he expect as results? What does he think? does he really want to leave you for the rest of the time doing nothing?

I think, to say that a technique B is not useful to solve A can be a PhD alone. Are you really sure that it is not? Did you exploit EVERY possibility? Or are you just not interested in finding the right way but are more interested to do something that works?

Even dough in the end, Einstein, Newton, Gauss and Shannon are the ones who were right, but never forget about the hundreds and thousands of people, that tried another approach/theory but found out it was a dead end! This is research. Writing that and why some approach not works is as valuable as finding the right path. Mostly regarding such a big field as you mentioned.

If you can really state that there is no way to use this technique (and propose other/ use similar techniques like ANN or ML in general), that's already good research. I really think, you just don't like to do a PhD at all?


  I do not wish to be an academician and investing my time on training myself in such a work is not in sync with my aspirations


Three possibilities:


give up the PhD and go into industry. There are a lot of companies which do research, mostly in the field I assume you are working on (machine learning, big data?)
Ask your supervisor what you should do next and stick with this PhD
if you really find another topic, which you find very interesting (even if it may fails! that's academic research, stick with it...), you may start a new PhD. But I would only suggest that if you do it for the fun of research and not as training. What you actually train as a PhD is doing research on your own. So don't expect the same experience and usefulness for the industry you would earn in the industry.