I'm looking for an "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms, both actual and theoretical.
Links are welcome, but please provide some explanation.
I'm looking for an "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms, both actual and theoretical.
Links are welcome, but please provide some explanation.
Your question suggests a confusion of techniques, representations and problems.
Neural Networks are a representation that can be used to approximate functions. A neural network approximates a function that maps from inputs to outputs by optimizing parameters (weights).
Genetic Algorithms are a technique that can be used to optimize a problem. You might chose to use a GA to optimize the weights in a neural network for instance. Or you might use it to optimize a different representation or approximation of a function.
Reinforcement Learning is a problem. In a Reinforcement learning problem, the agent learns a function mapping states to actions. You can learn this function directly in some problem domains, or by a near-direct approximation (like tile-coding), or with a function appropriator (like a neural network).