AutoSampler Causes An Error On The Second Optimization

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Expected Behavior

As an Optuna user, I expect to run multiple multi-objective optimizations with AutoSampler without any issues. The first optimization should run smoothly, and subsequent optimizations should also run without errors.

Environment

  • Optuna Version: 4.3.0
  • OptunaHub Version: 0.2.0
  • Python Version: 3.10.11
  • OS: Windows-10-10.0.26100-SP0

Error Messages, Stack Traces, or Logs

When running the second optimization, I encounter the following error:

[W 2025-05-17 14:35:50,881] Trial 52 failed with value None.
Traceback (most recent call last):
  File "c:\Users\hiroa\Desktop\hubtest\auto.py", line 17, in <module>
    study.optimize(objective, n_trials=100)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\study.py", line 475, in optimize
    _optimize(
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 63, in _optimize
    _optimize_sequential(
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 160, in _optimize_sequential
    frozen_trial = _run_trial(study, func, catch)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 248, in _run_trial
    raise func_err
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 197, in _run_trial
    value_or_values = func(trial)
  File "c:\Users\hiroa\Desktop\hubtest\auto.py", line 6, in objective
    x = trial.suggest_float("x", -5, 5)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 161, in suggest_float
    suggested_value = self._suggest(name, distribution)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 627, in _suggest
    elif self._is_relative_param(name, distribution):
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 659, in _is_relative_param
    if name not in self.relative_params:
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 71, in relative_params
    self._relative_params = self.study.sampler.sample_relative(
  File "C:\Users\hiroa\AppData\Local\optunahub\cache\api.github.com\optuna\optunahub-registry\main\package\samplers/auto_sampler\_sampler.py", line 222, in sample_relative
    return self._sampler.sample_relative(study, trial, search_space)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\nsgaii\_sampler.py", line 269, in sample_relative
    parent_population = self.get_parent_population(study, generation)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\_ga\_base.py", line 174, in get_parent_population
    return [trials[trial_id] for trial_id in cached_parent_population_ids]
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\_ga\_base.py", line 174, in <listcomp>
    return [trials[trial_id] for trial_id in cached_parent_population_ids]
IndexError: list index out of range

Steps to Reproduce

To reproduce the issue, follow these steps:

  1. Run the following code:
import optuna
import optunahub


def objective(trial):
    x = trial.suggest_float("x", -5, 5)
    y = trial.suggest_float("y", -5, 5)
    return x**2 + y**2, x


storage = optuna.storages.InMemoryStorage()
module = optunahub.load_module(package="samplers/auto_sampler")
study = optuna.create_study(sampler=module.AutoSampler(), storage=storage, directions=["minimize", "maximize"])
study.optimize(objective, n_trials=100)

study = optuna.create_study(sampler=module.AutoSampler(), storage=storage, directions=["minimize", "maximize"])
study.optimize(objective, n_trials=100)

Additional Context (Optional)

Based on the stack trace, it appears that the transition to the next generation in GA is not going well. This could be due to various reasons such as:

  • Insufficient population size: The population size might be too small to effectively transition to the next generation.
  • Poor initialization: The initial population might not be diverse enough to allow for effective evolution.
  • Convergence issues: The optimization process might be converging too quickly, leading to premature convergence and issues with transitioning to the next generation.

To resolve this issue, you can try the following:

  • Increase the population size: Try increasing the population size to see if it improves the transition to the next generation.
  • Improve initialization: Try using a different initialization method or a more diverse initial population to see if it improves the optimization process.
  • Modify the optimization process: Try modifying the optimization process to avoid premature convergence or to allow for more effective transition to the next generation.

Q: What is the expected behavior of AutoSampler in Optuna?

A: The expected behavior of AutoSampler in Optuna is to run multiple multi-objective optimizations without any issues. The first optimization should run smoothly, and subsequent optimizations should also run without errors.

Q: What are the system requirements for running AutoSampler in Optuna?

A: The system requirements for running AutoSampler in Optuna are:

  • Optuna Version: 4.3.0
  • OptunaHub Version: 0.2.0
  • Python Version: 3.10.11
  • OS: Windows-10-10.0.26100-SP0

Q: What is the error message when running the second optimization with AutoSampler?

A: The error message when running the second optimization with AutoSampler is:

[W 2025-05-17 14:35:50,881] Trial 52 failed with value None.
Traceback (most recent call last):
  File "c:\Users\hiroa\Desktop\hubtest\auto.py", line 17, in <module>
    study.optimize(objective, n_trials=100)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\study.py", line 475, in optimize
    _optimize(
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 63, in _optimize
    _optimize_sequential(
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 160, in _optimize_sequential
    frozen_trial = _run_trial(study, func, catch)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 248, in _run_trial
    raise func_err
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\study\_optimize.py", line 197, in _run_trial
    value_or_values = func(trial)
  File "c:\Users\hiroa\Desktop\hubtest\auto.py", line 6, in objective
    x = trial.suggest_float("x", -5, 5)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 161, in suggest_float
    suggested_value = self._suggest(name, distribution)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 627, in _suggest
    elif self._is_relative_param(name, distribution):
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 659, in _is_relative_param
    if name not in self.relative_params:
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\trial\_trial.py", line 71, in relative_params
    self._relative_params = self.study.sampler.sample_relative(
  File "C:\Users\hiroa\AppData\Local\optunahub\cache\api.github.com\optuna\optunahub-registry\main\package\samplers/auto_sampler\_sampler.py", line 222, in sample_relative
    return self._sampler.sample_relative(study, trial, search_space)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\nsgaii\_sampler.py", line 269, in sample_relative
    parent_population = self.get_parent_population(study, generation)
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\_ga\_base.py", line 174, in get_parent_population
    return [trials[trial_id] for trial_id in cached_parent_population_ids]
  File "C:\Users\hiroa\Desktop\hubtest\venv\lib\site-packages\optuna\samplers\_ga\_base.py", line 174, in <listcomp>
    return [trials[trial_id] for trial_id in cached_parent_population_ids]
IndexError: list index out of range

Q: How can I reproduce the issue with AutoSampler in Optuna?

A: To reproduce the issue with AutoSampler in Optuna, follow these steps:

  1. Run the following code:
import optuna
import optunahub


def objective(trial):
    x = trial.suggest_float("x", -5, 5)
    y = trial.suggest_float("y", -5, 5)
    return x**2 + y**2, x


storage = optuna.storages.InMemoryStorage()
module = optunahub.load_module(package="samplers/auto_sampler")
study = optuna.create_study(sampler=module.AutoSampler(), storage=storage, directions=["minimize", "maximize"])
study.optimize(objective, n_trials=100)

study = optuna.create_study(sampler=module.AutoSampler(), storage=storage, directions=["minimize", "maximize"])
study.optimize(objective, n_trials=100)

Q: What are some possible solutions to resolve the issue with AutoSampler in Optuna?

A: Some possible solutions to resolve the issue with AutoSampler in Optuna are:

  • Increase the population size: Try increasing the population size to see if it improves the transition to the next generation.
  • Improve initialization: Try using a different initialization method or a more diverse initial population to see if it improves the optimization process.
  • Modify the optimization process: Try modifying the optimization process to avoid premature convergence or to allow for more effective transition to the next generation.

By understanding the root cause of the issue and making the necessary adjustments, you should be able to resolve the problem and successfully run multiple multi-objective optimizations with AutoSampler.