I was introduced to genetic algorithms recently by this MSDN article, in which he calls them combinatorial evolution, but it seems to be the same thing, and am struggling to understand how combining two potential solutions will always produce a new solution that is at least as good as its parents.
Why is this so? Surely combining might produce something worse.
As far as I understand it, the algorithm is based on the concept that when a male and female of a species produce offspring, those offspring will have characteristics of both parents. Some combinations will be better, some worse and some just as good. The ones that are better (for whatever defintion of "better" is appropriate) stand more chance of surviving and producing offpsring that have the improved characteristics. However, there will be combinations that are weaker. Why isn't this an issue with GA?
However, there will be combinations that are weaker. Why isn't this an issue with GA?
-- Because the weaker combinations are discarded. – Robert Harvey Dec 4 '16 at 16:11Why isn't this an issue with GA?
Well, it is, or more exactly, it might be. One of the many (many) parameters to optimize using GAs is the population size : if it's too low, you might only produce weaker individuals, but if it's too high, the computation time associated with the fitness function might be too high. – Loufylouf Dec 5 '16 at 7:14