feat: 💥 Add _pseudo_ genetic search

The proposed implementation of a genetic algorithm for hyper optimization.

Even if genetic optimization might be costly for CNN, the applications in numeric analysis or Design of Experiment (DoE) make it still interesting.

Fixes: keras-team#47


Ref:

1. [Vishwakarma G, et al Towards Autonomous Machine Learning in Chemistry via Evolutionary Algorithms. **ChemRxiv.**](https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c7445a337d6c2849e26d98/original/towards-autonomous-machine-learning-in-chemistry-via-evolutionary-algorithms.pdf)
2. [Rosanna Nichols et al 2019 _Quantum Sci. Technol._ **4** 045012](https://iopscience.iop.org/article/10.1088/2058-9565/ab4d89/meta?casa_token=db7uZRqRMEAAAAAA:fRO9qB25dAkeoskS6MMyzpZw2jSiMkpsN4zA_k6lheWUXaSUU8fPS-JPMoNFcIl9tka4OPCG5AtDtiM)

Author: Anselmoo

keras_tuner/__init__.py

@@ -25,6 +25,7 @@
 from keras_tuner.engine.oracle import Oracle
 from keras_tuner.engine.tuner import Tuner
 from keras_tuner.tuners import BayesianOptimization
+from keras_tuner.tuners import GeneticOptimization
 from keras_tuner.tuners import GridSearch
 from keras_tuner.tuners import Hyperband
 from keras_tuner.tuners import RandomSearch

keras_tuner/tuners/__init__.py

@@ -14,6 +14,7 @@
 
 
 from keras_tuner.tuners.bayesian import BayesianOptimization
+from keras_tuner.tuners.genetic import GeneticOptimization
 from keras_tuner.tuners.gridsearch import GridSearch
 from keras_tuner.tuners.hyperband import Hyperband
 from keras_tuner.tuners.randomsearch import RandomSearch