Update optimizer.md

docs/tutorial/optimizer.md

@@ -1,9 +1,23 @@
 ## Choose an optimizer
 
-Your decision to use a specific optimizer should be based on the time it takes to evaluate a model and if you already have a start point. Try to stick to the following <b>guideline</b>, when choosing an optimizer:
+Your decision to use a specific optimizer should be based on the time it takes to evaluate a model and if you already have a start point. Try to stick to the following <b>guidelines</b>, when choosing an optimizer:
 - only use local or mcmc optimizers, if you have a <b>good start point</b>
 - random optimizers are a good way to <b>start exploring</b> the search space
 - the majority of the <b>iteration-time</b> should be the <b>evaluation-time</b> of the model
 
-You can choose an optimizer-class from the list provided in the [API](https://github.com/SimonBlanke/Hyperactive#hyperactive-api).
-All optimization techniques are explained in more detail [here](https://github.com/SimonBlanke/Hyperactive/tree/master/hyperactive/optimizers#optimization-techniques). A comparison between the iteration- and evaluation-time for different models can be seen [here](https://github.com/SimonBlanke/Hyperactive/tree/master/hyperactive/model#supported-packages).
+All optimization techniques are explained in more detail [here](https://simonblanke.github.io/Hyperactive/#/./optimizers/README?id=optimization-techniques). A comparison between the iteration- and evaluation-time for different models can be seen [here](https://simonblanke.github.io/Hyperactive/#/./performance/README?id=performance).
+You can choose the optimizer by passing one of the following strings to the 'optimizer' keyword in the Hyperactive-class:
+  - "HillClimbing"
+  - "StochasticHillClimbing"
+  - "TabuSearch"
+  - "RandomSearch"
+  - "RandomRestartHillClimbing"
+  - "RandomAnnealing"
+  - "SimulatedAnnealing",
+  - "StochasticTunneling"
+  - "ParallelTempering"
+  - "ParticleSwarm"
+  - "EvolutionStrategy"
+  - "Bayesian"
+
+