Update evolution doc (microsoft#1493)

* update readme in ga_squad

* update readme

* fix typo

* Update README.md

* Update README.md

* Update README.md

* update readme

* update

* fix path

* update reference

* fix bug in config file

* update nni_arch_overview.png

* update

* update

* update

* update home page

* update default value of metis tuner

* fix broken link in CommunitySharings

* update docs about nested search space

* update docs

* rename cascding to nested

* fix broken link

* update

* update issue link

* fix typo

* update evaluate parameters from GMM

* refine code

* fix optimized mode bug

* update import warning

* update warning

* update optimized mode

* update population_size in doc

docs/en_US/Tuner/BuiltinTuner.md

@@ -117,6 +117,11 @@ tuner:
 
 Its requirement of computation resource is relatively high. Specifically, it requires large initial population to avoid falling into local optimum. If your trial is short or leverages assessor, this tuner is a good choice. And, it is more suggested when your trial code supports weight transfer, that is, the trial could inherit the converged weights from its parent(s). This can greatly speed up the training progress. [Detailed Description](./EvolutionTuner.md)
 
+**Requirement of classArg**
+
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
+
+* **population_size** (*int value(should >0), optional, default = 20*) - the initial size of the population(trial num) in evolution tuner.
 
 **Usage example**
 
@@ -126,6 +131,7 @@ tuner:
   builtinTunerName: Evolution
   classArgs:
     optimize_mode: maximize
+    population_size: 100
 ```
 
 <br>