I've tried to research about this but couldn't find a satisfying answer. How does the length of an individuals DNA impact on the overall performance of the GA?
Imagine that I'm trying to find a solution for a combinatorial problem with many dimensions (x, y, z, w) for example. Representing the DNA in binary would yield a very long sequence.. are there any suggestions to how far I am allowed to go?
In my opinion the search space (should) increase exponentially with the amount of elements exposed to mutation, am I wrong?
What are some guidelines or techniques (preferably derived from experience) that somebody could provide on how to reduce the length of the DNA?
The GA specific operations such as crossover and mutation should not take long time even for very large chromosome sizes, since they are computationally simple operations (clone a matrix, change a bit of a matrix, etc).
Most of the time (I have seen around 85-95%) will be spent running the evaluation function for your individuals. Is entirely up to the specific problem you are solving to determine if a large DNA will impact the performance or not. Also, depending on the problem you try to solve, it may be impossible to shorten the size of the possible results.