When should I use genetic algorithms as opposed to neural networks?
Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice-versa) to solve a problem?
I know there are cases in which you can have both methods mixed, but I am looking for a high-level comparison between the two methods.
Answer
From wikipedia:
A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems.
and:
Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
If you have a problem where you can quantify the worth of a solution, a genetic algorithm can perform a directed search of the solution space. (E.g. find the shortest route between two points)
When you have a number of items in different classes, a neural network can "learn" to classify items it has not "seen" before. (E.g. face recognition, voice recognition)
Execution times must also be considered. A genetic algorithm takes a long time to find an acceptable solution. A neural network takes a long time to "learn", but then it can almost instantly classify new inputs.