class_docstr: real-coding genetic algorithm
  
  Parameters
  ----------------
  func : function
      The func you want to do optimal
  n_dim : int
      number of variables of func
  size_pop : int
      Size of population
  max_iter : int
      Max of iter
  prob_mut : float between 0 and 1
      Probability of mutation
  prob_cros : float between 0 and 1
      Probability of crossover
  lb : array_like
      The lower bound of every variables of func
  ub : array_like
      The upper bound of every variables of func
  n_processes : int
      Number of processes, 0 means use all cpu
code: def crtbp(self):
      # create the population, random floating point numbers of 0 ~ 1
      self.Chrom = np.random.random([self.size_pop, self.n_dim])
      return self.Chrom
  
imports: import numpy
  from .base import SkoBase
  from sko.tools import func_transformer
  from abc import ABCMeta, abstractmethod
  from .operators import crossover, mutation, ranking, selection
label_desc: Use image node_id 2 for calling the RCGA obj's underlying member method code with example usage: obj.crtbp() and returns: self