class_docstr: Replay Trace Search Strategy is a search strategy that always replays the trace by removing its
  decisions so that the decisions would be randomly re-generated.
  
  Parameters
  ----------
  population_size : int
      The initial population of traces from measured samples and randomly generated samples.
  init_measured_ratio : int
      The ratio of measured samples in the initial population.
  init_min_unmeasured : int
      The minimal size of unmeasured population in the initial sampling.
  max_fail_count : int
      The maximum number of failure during initial sampling.
  genetic_num_iters : int
      The number of iterations for genetic algorithm.
  genetic_mutate_prob : float
      The probability of mutation.
  genetic_max_fail_count : int
      The maximum number to retry mutation.
  eps_greedy : float
      The ratio of greedy selected samples in the final picks.
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docstr: Constructor
docstr_tok: ["Constructor"]
code: def __init__(
      self,
      *,
      population_size: int = 512,
      init_measured_ratio: float = 0.2,
      init_min_unmeasured: int = 50,
      max_fail_count: int = 5,
      genetic_num_iters: int = 4,
      genetic_mutate_prob: float = 0.85,
      genetic_max_fail_count: int = 10,
      eps_greedy: float = 0.05,
  ) -> None:
      """Constructor"""
      self.__init_handle_by_constructor__(
          _ffi_api.SearchStrategyEvolutionarySearch,  # type: ignore # pylint: disable=no-member
          population_size,
          init_measured_ratio,
          init_min_unmeasured,
          max_fail_count,
          genetic_num_iters,
          genetic_mutate_prob,
          genetic_max_fail_count,
          eps_greedy,
      )
  
imports: from tvm._ffi import register_object
  from ..None import _ffi_api
  from .search_strategy import SearchStrategy
label_desc: Use image node_id 1 to create a new EvolutionarySearch object from inherited base classes: SearchStrategy with example: obj = EvolutionarySearch()