Sklearn-genetic-opt

Features:
* Enabled deap's eaMuPlusLambda algorithm for the optimization process, now is the default routine
* Added the parameter keep_top_k to control the amout of solutions if the hall of fame (hof)
* Changed default parameters crossover_probability from 1 to 0.8 and generations from 50 to 40

Internal
* Changed parameters with pre-defined options to use pydantic models

Fixes
* Fix log of the scoring metric in logbook, now is part of the parameters and is show only once

Features:
* Added a logbook and history properties to the fitted GASearchCV to make post-fit analysis
* Elitism = False now implements a roulette selection instead of ignoring the parameter


API Changes:
* Refactored the optimization algorithm to use deap package instead of a custom implementation, this causes the removal of several methods, properties and variables inside the GASearchCV class
* The parameter encoding_length has been removed, it's not longer required to the GASearchCV class
* Renamed the property of the fitted estimator from best_params_ to best_params
* The verbosity now prints the deap log of the fitness function, it's standard deviation, max and min values from each generation
* The variable GASearchCV._best_solutions was removed and it's meant to be replaced with GASearchCV.logbook and GASearchCV.history

Bug Fixes:
* Fixs unexpected overwrites over if statements
* Correct validation when parameters dicts are empty

Enchacements:
* Criteria parameter to control if it's a minimization or maximization problem with respect to the scoring metric
* Plot fitness function over generations
* Unit tests for all the package
* Examples with regression problems
* Implementation of some magic methods
* Documentation of the GASearchCV class parameters

Features:
* GASearchCV for sklearn classifiers and regressors hyperparameters search using genetic algorithms and cross validation