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I have question. As in topic what is a practical usage of evolutionary algorithms. I know that they find the extremum of function and we can solve the travelling salesman problem ([link])1. Does it find application in other fields for example electronics, economy, medicine?

  • To get the interest of people, try adding some examples. For example: what is the 'extremum of function' and what is the 'traveling salesman problem'. Don't reply to me, I know what you mean, but add it to your post so you can reach a larger audience. – Bram Vanroy Nov 09 '15 at 19:10
  • Do you want a list of examples? Or is one (like [antenna design](https://en.wikipedia.org/wiki/Evolved_antenna)) enough? – Beta Nov 09 '15 at 19:14
  • @Beta some of them will be nice –  Nov 09 '15 at 19:47
  • It isn't hard to Google this. A search using your question uncovers hundreds of applications in just a few minutes of searching. – John Coleman Nov 09 '15 at 19:54
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    Possible duplicate of [What are good examples of genetic algorithms/genetic programming solutions?](http://stackoverflow.com/questions/1538235/what-are-good-examples-of-genetic-algorithms-genetic-programming-solutions) – manlio Mar 25 '17 at 20:54

3 Answers3

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The pro:
Evolutionary algorithms (EAs), as genetic algorithms (GAs), are general purpose optimization algorithms that can be applied to any problem for which you can define a fitness function. They have been applied to nearly all conceivable optimization problems, see e.g. the conference series on „Parallel Problem Solving from Nature“.

The con:
EAs are generally much slower than any specific optimization algorithm adapted to the problem. I would apply them only if all standard optimization algorithms fail.

Reinhard Männer
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this is really hard question to answer. It depends on what do you want to do with EA/GA. It is usable in electronics, physics, biotechnology etc.

You have basicly wide area of usage. You "just" need to make a fitness function and that's it, but this task is never so easy. Fitness means that you need to somehow rate every result of algorithm. For example, you want to find some coefficient in physics equation or some ordinary equation. You need to know which coefficients are good and bad... so you need some boundaries etc.

So the answer is yes, but depends on what do you need.

Apuna12
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The practical usage of GAs is to find good solutions to complex problems. They can be used in any field where a combinatorial optimization problem needs to be solved. GAs tend to be slower than other approaches, so in general they should be used when execution time is not a limiting factor (e.g., if you have 10 minutes to solve a problem then another approach such as a tabu search may be better, but if you have 3 days then GAs may be better).

For example, Design Space Exploration (DSE), which often has a very large search space with multiple disconnected feasible regions, would be a good candidate for GAs.

Philippe Olivier
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