class_docstr: Simulated Binary Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`.
  
  Generates a child from two parent individuals
  according to the polynomial probability distribution.
  
  - `Deb, K. and R. Agrawal.
    “Simulated Binary Crossover for Continuous Search Space.”
    Complex Syst. 9 (1995): n. pag.
    <https://www.complex-systems.com/abstracts/v09_i02_a02/>`_
  
  Args:
      eta:
          Distribution index. A small value of ``eta`` allows distant solutions
          to be selected as children solutions. If not specified, takes default
          value of ``2`` for single objective functions and ``20`` for multi objective.
code: def __init__(self, eta: Optional[float] = None) -> None:
      self._eta = eta
  
file_path: optuna/optuna/samplers/nsgaii/_crossovers/_sbx.py
imports: from typing import Optional
  import numpy
  from optuna._experimental import experimental_class
  from optuna.samplers.nsgaii._crossovers._base import BaseCrossover
  from optuna.study import Study
label_desc: Use image node_id 1 to create a new SBXCrossover object from inherited base classes: BaseCrossover with example: obj = SBXCrossover(eta)