Talk:Evolutionary algorithm: Difference between revisions

 

: Looks like it's within the letter of the task, but as to the spirit, I don't know. Intuition tells me that knowing the true distance to optimal will help avoid problems of local minima, and some (but certainly not all!) problems that evolutionary algorithms are applied to have true distance (or a close approximation of such) available. This may be a good case for splitting the task and specifying a goal-agnostic algorithm. --[[User:Short Circuit|Michael Mol]] 16:19, 1 September 2010 (UTC)

 

: In a real problem, you've got a high-dimensional space that you're searching and the fitness function is only poorly known (the profusion of species is clear demonstration that there are many local minima in the problem space that is biology). However, the only effect of varying the mutation rate with fitness, given that we have a reasonable metric, is that it results in faster convergence with smaller populations at each step. It doesn't change the fact that you're ''still'' having to do the evolution towards a solution through random variation and selection, which is the whole point. –[[User:Dkf|Donal Fellows]] 08:26, 2 September 2010 (UTC)