Observations
There are several general observations about the generation of solutions specifically via a genetic algorithm:
- Selection is clearly an important genetic operator, but opinion is divided over the importance of crossover versus mutation. There are many references in Fogel (2006) that support the importance of mutation-based search.
- As with all current machine learning problems it is worth tuning the parameters such as mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non-ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions unless there is elitist selection. There are theoretical but not yet practical upper and lower bounds for these parameters that can help guide selection.
- Often, GAs can rapidly locate good solutions, even for large search spaces. The same is of course also true for evolution strategies and evolutionary programming.
Read more about this topic: Genetic Algorithm
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