I have a pretty simple question. However I have searched extensively and am unable to find the answer. Is a genetic algorithm considered to be a form of unsupervised learning? I know that the algorithms evolves independently, however the fitness of each individual in the population is regularly measured (supervised?).
The objective of my algorithm is to optimize a set of heuristic weights via a genetic algorithm.
Thank you for your help! —
On
From http://en.wikipedia.org/wiki/Unsupervised_learning
In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.
From which it's pretty clear that genetic algorithms are not unsupervised as they are measured against a fitness criteria. Individual mutations may not be supervised, but the system as a whole is supervised as mutations are either removed or built upon based on the resulting fitness they give the algorithm.
From http://en.wikipedia.org/wiki/Reinforcement_learning
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, statistics, and genetic algorithms.
Which would sort of suggest that genetic algorithms are considered to fall under reinforcement learning.
Genetic Algorithms can be used for both supervised and unsupervised learning, e.g.:
If you have labeled training data or tagged examples, then you are using supervised training.