class_docstr: Modified Simulated Binary Crossover operation used by
  :class:`~optuna.samplers.NSGAIISampler`.
  
  vSBX generates child individuals without excluding any region of the parameter space,
  while maintaining the excellent properties of SBX.
  
  - `Pedro J. Ballester, Jonathan N. Carter.
    Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions
    in Multi-modal Optimization. GECCO 2003: 706-717
    <https://link.springer.com/chapter/10.1007/3-540-45105-6_86>`_
  
  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/_vsbx.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 VSBXCrossover object from inherited base classes: BaseCrossover with example: obj = VSBXCrossover(eta)