diff --git a/src/mlrose_ky/__init__.py b/src/mlrose_ky/__init__.py
index bb124c3e..a6b36e10 100644
--- a/src/mlrose_ky/__init__.py
+++ b/src/mlrose_ky/__init__.py
@@ -1,4 +1,4 @@
-"""MLROSe initialization file."""
+"""mlrose-ky initialization file."""
 
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
diff --git a/src/mlrose_ky/algorithms/crossovers/_crossover_base.py b/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
index 2c8f270d..5ea36553 100644
--- a/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
+++ b/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
@@ -34,7 +34,7 @@ class _CrossoverBase(ABC):
         'length' property.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
+    def __init__(self, optimization_problem: Any):
         """
         Initialize the CrossoverBase with the given optimization problem.
 
diff --git a/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py b/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
index eaf031cf..ab498edf 100644
--- a/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
@@ -26,7 +26,7 @@ class OnePointCrossover(_CrossoverBase):
     _CrossoverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
+    def __init__(self, optimization_problem: Any):
         """
         Initialize the OnePointCrossover with the given optimization problem.
 
diff --git a/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py b/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
index 7ba275b9..e29b9c5f 100644
--- a/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
@@ -27,7 +27,7 @@ class TSPCrossover(_CrossoverBase):
     _CrossoverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
+    def __init__(self, optimization_problem: Any):
         """
         Initialize the TSPCrossover with the given optimization problem.
 
diff --git a/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py b/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
index 573dbf15..5c7f247e 100644
--- a/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
@@ -27,7 +27,7 @@ class UniformCrossover(_CrossoverBase):
     _CrossoverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
+    def __init__(self, optimization_problem: Any):
         """
         Initialize the UniformCrossover with the given optimization problem.
 
diff --git a/src/mlrose_ky/algorithms/decay/arithmetic_decay.py b/src/mlrose_ky/algorithms/decay/arithmetic_decay.py
index fcc64159..433fd5b8 100644
--- a/src/mlrose_ky/algorithms/decay/arithmetic_decay.py
+++ b/src/mlrose_ky/algorithms/decay/arithmetic_decay.py
@@ -41,7 +41,7 @@ class ArithDecay:
     5.25
     """
 
-    def __init__(self, init_temp: float = 1.0, decay: float = 0.0001, min_temp: float = 0.001) -> None:
+    def __init__(self, init_temp: float = 1.0, decay: float = 0.0001, min_temp: float = 0.001):
         self.init_temp: float = init_temp
         self.decay: float = decay
         self.min_temp: float = min_temp
diff --git a/src/mlrose_ky/algorithms/decay/custom_decay.py b/src/mlrose_ky/algorithms/decay/custom_decay.py
index 772faabd..35fcc064 100644
--- a/src/mlrose_ky/algorithms/decay/custom_decay.py
+++ b/src/mlrose_ky/algorithms/decay/custom_decay.py
@@ -29,7 +29,7 @@ class CustomSchedule:
     15
     """
 
-    def __init__(self, schedule: Callable[..., float], **kwargs) -> None:
+    def __init__(self, schedule: Callable[..., float], **kwargs):
         self.schedule: Callable[..., float] = schedule
         self.kwargs: dict = kwargs
 
diff --git a/src/mlrose_ky/algorithms/decay/exponential_decay.py b/src/mlrose_ky/algorithms/decay/exponential_decay.py
index ff24d114..65f48c86 100644
--- a/src/mlrose_ky/algorithms/decay/exponential_decay.py
+++ b/src/mlrose_ky/algorithms/decay/exponential_decay.py
@@ -43,7 +43,7 @@ class ExpDecay:
     7.788007830714049
     """
 
-    def __init__(self, init_temp: float = 1.0, exp_const: float = 0.005, min_temp: float = 0.001) -> None:
+    def __init__(self, init_temp: float = 1.0, exp_const: float = 0.005, min_temp: float = 0.001):
         self.init_temp: float = init_temp
         self.exp_const: float = exp_const
         self.min_temp: float = min_temp
diff --git a/src/mlrose_ky/algorithms/decay/geometric_decay.py b/src/mlrose_ky/algorithms/decay/geometric_decay.py
index 2fb1bb25..218196e1 100644
--- a/src/mlrose_ky/algorithms/decay/geometric_decay.py
+++ b/src/mlrose_ky/algorithms/decay/geometric_decay.py
@@ -41,7 +41,7 @@ class GeomDecay:
     7.737809374999998
     """
 
-    def __init__(self, init_temp: float = 1.0, decay: float = 0.99, min_temp: float = 0.001) -> None:
+    def __init__(self, init_temp: float = 1.0, decay: float = 0.99, min_temp: float = 0.001):
         self.init_temp: float = init_temp
         self.decay: float = decay
         self.min_temp: float = min_temp
diff --git a/src/mlrose_ky/algorithms/mutators/_mutator_base.py b/src/mlrose_ky/algorithms/mutators/_mutator_base.py
index fd7c4a03..9b3aaeec 100644
--- a/src/mlrose_ky/algorithms/mutators/_mutator_base.py
+++ b/src/mlrose_ky/algorithms/mutators/_mutator_base.py
@@ -33,8 +33,9 @@ class _MutatorBase(ABC):
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, opt_prob: Any) -> None:
+    def __init__(self, opt_prob: Any):
         super().__init__()
+
         self._opt_prob: Any = opt_prob
         self._length: int = opt_prob.length
 
diff --git a/src/mlrose_ky/algorithms/mutators/discrete_mutator.py b/src/mlrose_ky/algorithms/mutators/discrete_mutator.py
index e5ed35a4..87f0d89a 100644
--- a/src/mlrose_ky/algorithms/mutators/discrete_mutator.py
+++ b/src/mlrose_ky/algorithms/mutators/discrete_mutator.py
@@ -30,8 +30,9 @@ class DiscreteMutator(_MutatorBase):
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, opt_prob: Any) -> None:
+    def __init__(self, opt_prob: Any):
         super().__init__(opt_prob)
+
         self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
diff --git a/src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py b/src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py
index c7a9e56e..ae5c9add 100644
--- a/src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py
+++ b/src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py
@@ -28,7 +28,7 @@ class SwapMutator(_MutatorBase):
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, opt_prob: Any) -> None:
+    def __init__(self, opt_prob: Any):
         super().__init__(opt_prob)
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
diff --git a/src/mlrose_ky/algorithms/mutators/single_gene_mutator.py b/src/mlrose_ky/algorithms/mutators/single_gene_mutator.py
index 55f76f04..031dc7f1 100644
--- a/src/mlrose_ky/algorithms/mutators/single_gene_mutator.py
+++ b/src/mlrose_ky/algorithms/mutators/single_gene_mutator.py
@@ -29,8 +29,9 @@ class ChangeOneMutator(_MutatorBase):
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, opt_prob: Any) -> None:
+    def __init__(self, opt_prob: Any):
         super().__init__(opt_prob)
+
         self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
diff --git a/src/mlrose_ky/algorithms/mutators/single_shift_mutator.py b/src/mlrose_ky/algorithms/mutators/single_shift_mutator.py
index d36d55b6..4657fe33 100644
--- a/src/mlrose_ky/algorithms/mutators/single_shift_mutator.py
+++ b/src/mlrose_ky/algorithms/mutators/single_shift_mutator.py
@@ -30,8 +30,9 @@ class ShiftOneMutator(_MutatorBase):
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, opt_prob: Any) -> None:
+    def __init__(self, opt_prob: Any):
         super().__init__(opt_prob)
+
         self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
diff --git a/src/mlrose_ky/fitness/continuous_peaks.py b/src/mlrose_ky/fitness/continuous_peaks.py
index f5ff837d..c1eb91fc 100644
--- a/src/mlrose_ky/fitness/continuous_peaks.py
+++ b/src/mlrose_ky/fitness/continuous_peaks.py
@@ -37,7 +37,7 @@ class ContinuousPeaks:
     with `max_val = 2`) optimization problems only.
     """
 
-    def __init__(self, t_pct: float = 0.1) -> None:
+    def __init__(self, t_pct: float = 0.1):
         self.t_pct: float = t_pct
         self.prob_type: str = "discrete"
 
diff --git a/src/mlrose_ky/fitness/max_k_color.py b/src/mlrose_ky/fitness/max_k_color.py
index fedd8b18..a7b36f49 100644
--- a/src/mlrose_ky/fitness/max_k_color.py
+++ b/src/mlrose_ky/fitness/max_k_color.py
@@ -110,7 +110,7 @@ def get_prob_type(self) -> str:
         """
         return self.prob_type
 
-    def set_graph(self, graph) -> None:
+    def set_graph(self, graph):
         """Set the graph edges from an external graph representation.
 
         Parameters
diff --git a/src/mlrose_ky/fitness/one_max.py b/src/mlrose_ky/fitness/one_max.py
index 51c1bae5..b708e478 100644
--- a/src/mlrose_ky/fitness/one_max.py
+++ b/src/mlrose_ky/fitness/one_max.py
@@ -27,7 +27,7 @@ class OneMax:
     The One Max fitness function is suitable for use in either discrete or continuous-state optimization problems.
     """
 
-    def __init__(self) -> None:
+    def __init__(self):
         self.prob_type: str = "either"
 
     @staticmethod
diff --git a/src/mlrose_ky/fitness/travelling_salesperson.py b/src/mlrose_ky/fitness/travelling_salesperson.py
index 7f3f572e..f275fec0 100644
--- a/src/mlrose_ky/fitness/travelling_salesperson.py
+++ b/src/mlrose_ky/fitness/travelling_salesperson.py
@@ -51,7 +51,7 @@ class TravellingSalesperson:
        fitness function object.
     """
 
-    def __init__(self, coords: list[tuple] = None, distances: list[tuple] = None) -> None:
+    def __init__(self, coords: list[tuple] = None, distances: list[tuple] = None):
         # Ensure that at least one of coords or distances is provided
         if coords is None and distances is None:
             raise ValueError("At least one of coords and distances must be specified.")
diff --git a/src/mlrose_ky/gridsearch/grid_search_mixin.py b/src/mlrose_ky/gridsearch/grid_search_mixin.py
index 7219aaee..b05e6a65 100644
--- a/src/mlrose_ky/gridsearch/grid_search_mixin.py
+++ b/src/mlrose_ky/gridsearch/grid_search_mixin.py
@@ -27,7 +27,7 @@ class GridSearchMixin:
         Flag indicating whether to apply argmax to predictions and ground truths before scoring.
     """
 
-    def __init__(self, scorer_method: Callable = None) -> None:
+    def __init__(self, scorer_method: Callable = None):
         """
         Initializes the GridSearchMixin with a specified scoring method.
 
diff --git a/src/mlrose_ky/neural/_nn_base.py b/src/mlrose_ky/neural/_nn_base.py
index f28a1c5a..0bef1422 100644
--- a/src/mlrose_ky/neural/_nn_base.py
+++ b/src/mlrose_ky/neural/_nn_base.py
@@ -27,7 +27,7 @@ def __init__(self):
         pass
 
     @abstractmethod
-    def fit(self, X: np.ndarray, y: np.ndarray = None, init_weights: np.ndarray = None) -> None:
+    def fit(self, X: np.ndarray, y: np.ndarray = None, init_weights: np.ndarray = None):
         """
         Fit the neural network to the data.
 
diff --git a/src/mlrose_ky/neural/_nn_core.py b/src/mlrose_ky/neural/_nn_core.py
index c8fdb652..181e5e38 100644
--- a/src/mlrose_ky/neural/_nn_core.py
+++ b/src/mlrose_ky/neural/_nn_core.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 from sklearn.preprocessing import LabelBinarizer
+
 from mlrose_ky.algorithms.decay import GeomDecay
 from mlrose_ky.algorithms.ga import genetic_alg
 from mlrose_ky.algorithms.rhc import random_hill_climb
@@ -71,7 +72,7 @@ def __init__(
         self.predicted_probs: np.ndarray = np.array([])
         self.fitness_curve: list[float] = []
 
-    def _validate(self) -> None:
+    def _validate(self):
         """Validate the model parameters."""
         if (not isinstance(self.max_iters, int) and self.max_iters != np.inf) or self.max_iters < 0:
             raise ValueError(f"max_iters must be a positive integer, got {self.max_iters}.")
@@ -109,7 +110,7 @@ def _validate(self) -> None:
                 f"'random_hill_climb', 'simulated_annealing', 'genetic_alg', 'gradient_descent', got {self.algorithm}."
             )
 
-    def _validate_input(self, y: np.ndarray) -> None:
+    def _validate_input(self, y: np.ndarray):
         """
         Add classes_ attribute based on classes present in y.
 
diff --git a/src/mlrose_ky/neural/activation/identity.py b/src/mlrose_ky/neural/activation/identity.py
index 77d7e78c..167083a2 100644
--- a/src/mlrose_ky/neural/activation/identity.py
+++ b/src/mlrose_ky/neural/activation/identity.py
@@ -4,6 +4,7 @@
 # License: BSD 3-clause
 
 import warnings
+
 import numpy as np
 
 from mlrose_ky.decorators import short_name
diff --git a/src/mlrose_ky/neural/linear_regression.py b/src/mlrose_ky/neural/linear_regression.py
index de28d4b9..df323962 100644
--- a/src/mlrose_ky/neural/linear_regression.py
+++ b/src/mlrose_ky/neural/linear_regression.py
@@ -4,6 +4,7 @@
 # License: BSD 3-clause
 
 from sklearn.base import RegressorMixin
+
 from mlrose_ky.algorithms.decay import GeomDecay
 from ._nn_core import _NNCore
 
diff --git a/src/mlrose_ky/neural/nn_classifier.py b/src/mlrose_ky/neural/nn_classifier.py
index c5a72727..f192f49f 100644
--- a/src/mlrose_ky/neural/nn_classifier.py
+++ b/src/mlrose_ky/neural/nn_classifier.py
@@ -3,9 +3,11 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
+from typing import Any, Optional, Callable
+
 import numpy as np
+
 from mlrose_ky.neural._nn_base import _NNBase
-from typing import Any, Optional, Callable
 
 
 class NNClassifier(_NNBase):
diff --git a/src/mlrose_ky/opt_probs/continuous_opt.py b/src/mlrose_ky/opt_probs/continuous_opt.py
index 75ef8285..f95bf0c1 100644
--- a/src/mlrose_ky/opt_probs/continuous_opt.py
+++ b/src/mlrose_ky/opt_probs/continuous_opt.py
@@ -2,14 +2,15 @@
 
 from typing import Any
 
-# Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
-# License: BSD 3-clause
-
 import numpy as np
 
 from mlrose_ky.opt_probs.opt_prob import _OptProb
 
 
+# Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
+# License: BSD 3-clause
+
+
 class ContinuousOpt(_OptProb):
     """Class for defining continuous-state optimization problems.
 
@@ -77,7 +78,7 @@ def calculate_updates(self) -> list:
         """
         return self.fitness_fn.calculate_updates()
 
-    def find_neighbors(self) -> None:
+    def find_neighbors(self):
         """Find all neighbors of the current state."""
         # Pre-allocate a NumPy array for neighbors (maximum of 2 * length neighbors)
         neighbors_matrix = np.zeros((2 * self.length, self.length))
@@ -145,7 +146,7 @@ def random_neighbor(self) -> np.ndarray:
 
         return neighbor
 
-    def random_pop(self, pop_size: int) -> None:
+    def random_pop(self, pop_size: int):
         """Create a population of random state vectors.
 
         Parameters
@@ -221,7 +222,7 @@ def reproduce(self, parent_1: np.ndarray, parent_2: np.ndarray, mutation_prob: f
 
         return child
 
-    def reset(self) -> None:
+    def reset(self):
         """Set the current state vector to a random value and reset its fitness."""
         self.state = self.random()
         self.fitness_evaluations = 0
diff --git a/src/mlrose_ky/opt_probs/discrete_opt.py b/src/mlrose_ky/opt_probs/discrete_opt.py
index e7227e10..153b2362 100644
--- a/src/mlrose_ky/opt_probs/discrete_opt.py
+++ b/src/mlrose_ky/opt_probs/discrete_opt.py
@@ -106,7 +106,7 @@ def __init__(
         self._mut_mask: np.ndarray | None = None
         self._mut_inf: np.ndarray | None = None
 
-    def eval_node_probs(self) -> None:
+    def eval_node_probs(self):
         """Update probability density estimates."""
         mutual_info = self._get_mutual_info_impl()
 
@@ -143,7 +143,7 @@ def eval_node_probs(self) -> None:
         self.node_probs = probs
         self.parent_nodes = parent
 
-    def set_mimic_fast_mode(self, fast_mode: bool) -> None:
+    def set_mimic_fast_mode(self, fast_mode: bool):
         """Enable or disable MIMIC fast mode."""
         if fast_mode:
             mut_mask = np.zeros([self.length, self.length], dtype=bool)
@@ -214,7 +214,7 @@ def _get_mutual_info_fast(self) -> np.ndarray:
 
         return mutual_info
 
-    def find_neighbors(self) -> None:
+    def find_neighbors(self):
         """Find all neighbors of the current state."""
         self.neighbors = []
 
@@ -233,7 +233,7 @@ def find_neighbors(self) -> None:
                     neighbor[i] = j
                     self.neighbors.append(neighbor)
 
-    def find_sample_order(self) -> None:
+    def find_sample_order(self):
         """Determine order in which to generate sample vector elements."""
         sample_order = []
         last = [0]
@@ -253,7 +253,7 @@ def find_sample_order(self) -> None:
 
         self.sample_order = sample_order
 
-    def find_top_pct(self, keep_pct: float) -> None:
+    def find_top_pct(self, keep_pct: float):
         """Select samples with fitness in the top keep_pct percentile.
 
         Parameters
@@ -318,7 +318,7 @@ def random_neighbor(self) -> np.ndarray:
 
         return neighbor
 
-    def random_pop(self, pop_size: int) -> None:
+    def random_pop(self, pop_size: int):
         """Create a population of random state vectors.
 
         Parameters
@@ -367,7 +367,7 @@ def reproduce(self, parent_1: np.ndarray, parent_2: np.ndarray, mutation_prob: f
         child = self._crossover.mate(parent_1, parent_2)
         return self._mutator.mutate(child, mutation_prob)
 
-    def reset(self) -> None:
+    def reset(self):
         """Set the current state vector to a random value and get its fitness."""
         self.state = self.random()
         self.fitness = self.eval_fitness(self.state)
diff --git a/src/mlrose_ky/opt_probs/flip_flop_opt.py b/src/mlrose_ky/opt_probs/flip_flop_opt.py
index 319a3882..1fe4a07d 100644
--- a/src/mlrose_ky/opt_probs/flip_flop_opt.py
+++ b/src/mlrose_ky/opt_probs/flip_flop_opt.py
@@ -80,11 +80,11 @@ def __init__(
         state = np.random.randint(2, size=self.length)
         self.set_state(state)
 
-    def evaluate_population_fitness(self) -> None:
+    def evaluate_population_fitness(self):
         """Calculate fitness for the current population."""
         self.pop_fitness = self.fitness_fn.evaluate_many(self.population)
 
-    def random_pop(self, pop_size: int) -> None:
+    def random_pop(self, pop_size: int):
         """Create a population of random state vectors.
 
         Parameters
diff --git a/src/mlrose_ky/opt_probs/opt_prob.py b/src/mlrose_ky/opt_probs/opt_prob.py
index b904d036..550063ee 100644
--- a/src/mlrose_ky/opt_probs/opt_prob.py
+++ b/src/mlrose_ky/opt_probs/opt_prob.py
@@ -113,7 +113,7 @@ def eval_fitness(self, state: np.ndarray) -> float:
         self.fitness_evaluations += 1
         return fitness
 
-    def eval_mate_probs(self) -> None:
+    def eval_mate_probs(self):
         """Calculate the probability of each member of the population reproducing."""
         pop_fitness = np.copy(self.pop_fitness)
 
@@ -210,7 +210,7 @@ def get_state(self) -> np.ndarray:
         """
         return self.state
 
-    def set_population(self, new_population: np.ndarray) -> None:
+    def set_population(self, new_population: np.ndarray):
         """Set a new population and evaluate its fitness.
 
         Parameters
@@ -221,11 +221,11 @@ def set_population(self, new_population: np.ndarray) -> None:
         self.population = new_population
         self.evaluate_population_fitness()
 
-    def evaluate_population_fitness(self) -> None:
+    def evaluate_population_fitness(self):
         """Evaluate the fitness of the current population."""
         self.pop_fitness = np.array([self.eval_fitness(indiv) for indiv in self.population])
 
-    def set_state(self, new_state: np.ndarray) -> None:
+    def set_state(self, new_state: np.ndarray):
         """Set a new state vector and evaluate its fitness.
 
         Parameters
diff --git a/src/mlrose_ky/opt_probs/queens_opt.py b/src/mlrose_ky/opt_probs/queens_opt.py
index f5c776ad..56a1d2cf 100644
--- a/src/mlrose_ky/opt_probs/queens_opt.py
+++ b/src/mlrose_ky/opt_probs/queens_opt.py
@@ -4,6 +4,7 @@
 # License: BSD 3-clause
 
 from typing import Any
+
 import numpy as np
 
 from mlrose_ky.algorithms.crossovers import UniformCrossover
diff --git a/src/mlrose_ky/opt_probs/tsp_opt.py b/src/mlrose_ky/opt_probs/tsp_opt.py
index eb496cff..eae0e4e9 100644
--- a/src/mlrose_ky/opt_probs/tsp_opt.py
+++ b/src/mlrose_ky/opt_probs/tsp_opt.py
@@ -96,7 +96,7 @@ def adjust_probs(probs: np.ndarray) -> np.ndarray:
         sp = np.sum(probs)
         return np.zeros(np.shape(probs)) if sp == 0 else probs / sp
 
-    def find_neighbors(self) -> None:
+    def find_neighbors(self):
         """Find all neighbors of the current state."""
         self.neighbors = []
 
diff --git a/src/mlrose_ky/runners/_nn_runner_base.py b/src/mlrose_ky/runners/_nn_runner_base.py
index 08db741e..e1879ee3 100644
--- a/src/mlrose_ky/runners/_nn_runner_base.py
+++ b/src/mlrose_ky/runners/_nn_runner_base.py
@@ -83,7 +83,7 @@ def __init__(
         n_jobs: int = 1,
         replay: bool = False,
         **kwargs,
-    ) -> None:
+    ):
         """
         Initializes the _NNRunnerBase class.
 
@@ -272,7 +272,7 @@ def _check_match(df_reference: pd.DataFrame, df_to_check: pd.DataFrame) -> bool:
 
         return False
 
-    def _tear_down(self, filename: str | None = None) -> None:
+    def _tear_down(self, filename: str | None = None):
         """
         Finalizes the runner, ensuring that the proper files are saved or cleaned up.
 
diff --git a/src/mlrose_ky/runners/_runner_base.py b/src/mlrose_ky/runners/_runner_base.py
index ec3d6b75..65e934b2 100644
--- a/src/mlrose_ky/runners/_runner_base.py
+++ b/src/mlrose_ky/runners/_runner_base.py
@@ -66,12 +66,12 @@ def get_dynamic_runner_name(self) -> str:
 
         return dynamic_runner_name
 
-    def set_dynamic_runner_name(self, name: str) -> None:
+    def set_dynamic_runner_name(self, name: str):
         """Set a dynamic runner name."""
         self._dynamic_short_name = name
 
     @staticmethod
-    def _print_banner(text: str) -> None:
+    def _print_banner(text: str):
         """Print a formatted banner for logging."""
         logging.info("*" * len(text))
         logging.info(text)
@@ -92,7 +92,7 @@ def _sanitize_value(value: Any) -> str:
         return sanitized_value
 
     @abstractmethod
-    def run(self) -> None:
+    def run(self):
         """Abstract method to be implemented by subclasses."""
         pass
 
@@ -109,7 +109,7 @@ def __init__(
         replay: bool = False,
         override_ctrl_c_handler: bool = True,
         **kwargs: Any,
-    ) -> None:
+    ):
         """
         Initialize the runner with required parameters, including problem setup,
         iteration controls, and output settings.
@@ -169,12 +169,12 @@ def __init__(
 
         self._increment_spawn_count()
 
-    def _increment_spawn_count(self) -> None:
+    def _increment_spawn_count(self):
         """Increment the spawn count for the runner (used in parallel execution)."""
         with self._spawn_count.get_lock():
             self._spawn_count.value += 1
 
-    def _decrement_spawn_count(self) -> None:
+    def _decrement_spawn_count(self):
         """Decrement the spawn count for the runner."""
         with self._spawn_count.get_lock():
             self._spawn_count.value -= 1
@@ -184,7 +184,7 @@ def get_spawn_count(self) -> int:
         self._print_banner(f"*** Spawn Count Remaining: {self._spawn_count.value} ***")
         return self._spawn_count.value
 
-    def abort(self) -> None:
+    def abort(self):
         """Set the abort flag to signal that the runner should stop execution."""
         self._print_banner("*** ABORTING ***")
 
@@ -195,7 +195,7 @@ def has_aborted(self) -> bool:
         """Return whether the abort flag has been set."""
         return self._abort_flag.value
 
-    def set_replay_mode(self, value: bool = True) -> None:
+    def set_replay_mode(self, value: bool = True):
         """Enable or disable replay mode, which reuses previous results."""
         with self._replay_mode.get_lock():
             self._replay_mode.value = value
@@ -204,7 +204,7 @@ def replay_mode(self) -> bool:
         """Check if replay mode is enabled."""
         return self._replay_mode.value
 
-    def _setup(self) -> None:
+    def _setup(self):
         """Prepare the runner by clearing stats, setting up directories, and handling Ctrl-C interrupts."""
         self._raw_run_stats = []
         self._fitness_curves = []
@@ -225,7 +225,7 @@ def _setup(self) -> None:
                 self._original_sigint_handler = signal.getsignal(signal.SIGINT)
                 signal.signal(signal.SIGINT, self._ctrl_c_handler)
 
-    def _ctrl_c_handler(self, sig: int, frame: Any) -> None:
+    def _ctrl_c_handler(self, sig: int, frame: Any):
         """
         Handle Ctrl-C interruptions by saving progress and aborting the run.
 
@@ -240,7 +240,7 @@ def _ctrl_c_handler(self, sig: int, frame: Any) -> None:
         self._sigint_params = (sig, frame)
         self.abort()
 
-    def _tear_down(self) -> None:
+    def _tear_down(self):
         """Clean up after the experiment, restoring signal handlers and managing resources."""
         if not self.override_ctrl_c_handler:
             return
@@ -256,7 +256,7 @@ def _tear_down(self) -> None:
         except (ValueError, TypeError, AttributeError, Exception) as e:
             logging.error(f"Problem restoring SIGINT handler: {e}")
 
-    def log_current_argument(self, arg_name: str, arg_value: Any) -> None:
+    def log_current_argument(self, arg_name: str, arg_value: Any):
         """Log the current argument passed to the algorithm."""
         self._current_logged_algorithm_args[arg_name] = arg_value
 
@@ -304,7 +304,7 @@ def run_experiment(self, algorithm: Any, **kwargs: Any) -> tuple[pd.DataFrame |
 
         return self.run_stats_df, self.curves_df
 
-    def _run_one_experiment(self, algorithm: Any, total_args: dict[str, Any], **params: Any) -> None:
+    def _run_one_experiment(self, algorithm: Any, total_args: dict[str, Any], **params: Any):
         """
         Execute a single iteration of the experiment.
 
@@ -332,14 +332,14 @@ def _run_one_experiment(self, algorithm: Any, total_args: dict[str, Any], **para
             **total_args,
         )
 
-    def _create_and_save_run_data_frames(self, extra_data_frames: dict[str, pd.DataFrame] = None, final_save: bool = False) -> None:
+    def _create_and_save_run_data_frames(self, extra_data_frames: dict[str, pd.DataFrame] = None, final_save: bool = False):
         """
         Save the collected run statistics and fitness curves to disk.
 
         Parameters
         ----------
         extra_data_frames : dict[str, pd.DataFrame], optional
-            Additional data frames to save.
+            Additional DataFrames to save.
         final_save : bool, optional
             Whether this is the final save of the experiment (default False).
         """
@@ -357,7 +357,7 @@ def _create_and_save_run_data_frames(self, extra_data_frames: dict[str, pd.DataF
                 for name, df in extra_data_frames.items():
                     self._dump_df_to_disk(df, df_name=name, final_save=final_save)
 
-    def _dump_df_to_disk(self, df: pd.DataFrame, df_name: str, final_save: bool = False) -> None:
+    def _dump_df_to_disk(self, df: pd.DataFrame, df_name: str, final_save: bool = False):
         """
         Save the DataFrame to disk as both a pickle and CSV file.
 
@@ -507,7 +507,7 @@ def _invoke_algorithm(
 
         return result
 
-    def start_run_timing(self) -> None:
+    def start_run_timing(self):
         """Start timing the experiment's execution."""
         self._run_start_time = time.perf_counter()
 
diff --git a/src/mlrose_ky/runners/ga_runner.py b/src/mlrose_ky/runners/ga_runner.py
index 8d713b4b..5095f3bc 100644
--- a/src/mlrose_ky/runners/ga_runner.py
+++ b/src/mlrose_ky/runners/ga_runner.py
@@ -1,15 +1,7 @@
-"""Class for running optimization experiments using Genetic Algorithms (GA), including grid search functionality."""
-
-# Authors: Andrew Rollings (modified by Kyle Nakamura)
-# License: BSD 3-clause
-
-import mlrose_ky
-from mlrose_ky.decorators import short_name
-from mlrose_ky.runners._runner_base import _RunnerBase
-
 """
-Example usage:
+Class for running optimization experiments using Genetic Algorithms (GA), including grid search functionality.
 
+Example usage:
     experiment_name = 'example_experiment'
     problem = TSPGenerator.generate(seed=SEED, number_of_cities=22)
 
@@ -21,29 +13,83 @@
                   max_attempts=1000,
                   population_sizes=[150, 200, 300],
                   mutation_rates=[0.4, 0.5, 0.6])
-                  
-    # the two data frames will contain the results
-    df_run_stats, df_run_curves = ga.run()                  
+
+    df_run_stats, df_run_curves = ga.run()
 """
 
+# Authors: Andrew Rollings (modified by Kyle Nakamura)
+# License: BSD 3-clause
+
+from typing import Any
+
+import pandas as pd
+
+import mlrose_ky
+from mlrose_ky.decorators import short_name
+from mlrose_ky.runners._runner_base import _RunnerBase
+
 
 @short_name("ga")
 class GARunner(_RunnerBase):
+    """
+    A runner for performing optimization experiments using Genetic Algorithms (GA).
+
+    This class extends _RunnerBase and provides functionality for running experiments
+    with Genetic Algorithms, including grid search over hyperparameters such as
+    population size and mutation rate.
+
+    Attributes
+    ----------
+    population_sizes : list[int]
+        List of population sizes to test in the grid search.
+    mutation_rates : list[float]
+        List of mutation rates to test in the grid search.
+    hamming_factors : Optional[list[float]]
+        Optional list of Hamming factors for the algorithm.
+    hamming_factor_decays : Optional[list[float]]
+        Optional list of Hamming factor decays.
+    """
 
     def __init__(
         self,
-        problem,
-        experiment_name,
-        seed,
-        iteration_list,
-        population_sizes,
-        mutation_rates,
-        hamming_factors=None,
-        hamming_factor_decays=None,
-        max_attempts=500,
-        generate_curves=True,
-        **kwargs,
+        problem: Any,
+        experiment_name: str,
+        seed: int,
+        iteration_list: list[int],
+        population_sizes: list[int],
+        mutation_rates: list[float],
+        hamming_factors: list[float] = None,
+        hamming_factor_decays: list[float] = None,
+        max_attempts: int = 500,
+        generate_curves: bool = True,
+        **kwargs: Any,
     ):
+        """
+        Initialize the GARunner class with problem data and various experiment parameters.
+
+        Parameters
+        ----------
+        problem : Any
+            The optimization problem to be solved.
+        experiment_name : str
+            Name of the experiment.
+        seed : int
+            Random seed for reproducibility.
+        iteration_list : list of int
+            List of iterations for the experiment.
+        population_sizes : list of int
+            List of population sizes to test in the grid search.
+        mutation_rates : list of float
+            List of mutation rates to test in the grid search.
+        hamming_factors : list of float, optional
+            Optional list of Hamming factors for the algorithm.
+        hamming_factor_decays : list of float, optional
+            Optional list of Hamming factor decays.
+        max_attempts : int, optional
+            Maximum number of attempts without improvement before stopping.
+        generate_curves : bool, optional
+            Whether to generate learning curves.
+        """
         super().__init__(
             problem=problem,
             experiment_name=experiment_name,
@@ -53,12 +99,24 @@ def __init__(
             generate_curves=generate_curves,
             **kwargs,
         )
-        self.population_sizes = population_sizes
-        self.mutation_rates = mutation_rates
-        self.hamming_factors = hamming_factors
-        self.hamming_factor_decays = hamming_factor_decays
+        self.population_sizes: list[int] = population_sizes
+        self.mutation_rates: list[float] = mutation_rates
+        self.hamming_factors: list[float] = hamming_factors
+        self.hamming_factor_decays: list[float] = hamming_factor_decays
+
+    def run(self) -> tuple[pd.DataFrame, pd.DataFrame]:
+        """
+        Run the genetic algorithm experiment.
+
+        This method performs grid search over the provided population sizes,
+        mutation rates, and optional Hamming factors and decays, and returns
+        the statistics and curves generated by the experiment.
 
-    def run(self):
+        Returns
+        -------
+        tuple
+            A tuple containing two DataFrames: run statistics and run curves
+        """
         return super().run_experiment(
             algorithm=mlrose_ky.genetic_alg,
             pop_size=("Population Size", self.population_sizes),
diff --git a/src/mlrose_ky/runners/mimic_runner.py b/src/mlrose_ky/runners/mimic_runner.py
index 6e9d3dca..513b873e 100644
--- a/src/mlrose_ky/runners/mimic_runner.py
+++ b/src/mlrose_ky/runners/mimic_runner.py
@@ -1,13 +1,6 @@
-"""Class for running optimization experiments using MIMIC, including grid search functionality."""
-
-# Authors: Andrew Rollings (modified by Kyle Nakamura)
-# License: BSD 3-clause
-
-import mlrose_ky
-from mlrose_ky.decorators import short_name
-from mlrose_ky.runners._runner_base import _RunnerBase
-
 """
+Class for running optimization experiments using MIMIC, including grid search functionality.
+
 Example usage:
 
     experiment_name = 'example_experiment'
@@ -20,28 +13,78 @@
                       iteration_list=2 ** np.arange(10),
                       max_attempts=500,
                       keep_percent_list=[0.25, 0.5, 0.75])
-                      
-    # the two data frames will contain the results
+
     df_run_stats, df_run_curves = mmc.run()
 """
 
+# Authors: Andrew Rollings (modified by Kyle Nakamura)
+# License: BSD 3-clause
+
+from typing import Any
+
+import pandas as pd
+
+import mlrose_ky
+from mlrose_ky.decorators import short_name
+from mlrose_ky.runners._runner_base import _RunnerBase
+
 
 @short_name("mimic")
 class MIMICRunner(_RunnerBase):
+    """
+    A runner for performing optimization experiments using MIMIC.
+
+    This class extends _RunnerBase and provides functionality for running experiments
+    with the MIMIC algorithm, including grid search over hyperparameters such as
+    population size and keep percent.
+
+    Attributes
+    ----------
+    keep_percent_list : list[float]
+        List of keep percentages to test in the grid search.
+    population_sizes : list[int]
+        List of population sizes to test in the grid search.
+    _use_fast_mimic : bool, optional
+        Whether to use the fast MIMIC mode, if available.
+    """
 
     def __init__(
         self,
-        problem,
-        experiment_name,
-        seed,
-        iteration_list,
-        population_sizes,
-        keep_percent_list,
-        max_attempts=5,
-        generate_curves=True,
-        use_fast_mimic=True,
-        **kwargs,
+        problem: Any,
+        experiment_name: str,
+        seed: int,
+        iteration_list: list[int],
+        population_sizes: list[int],
+        keep_percent_list: list[float],
+        max_attempts: int = 5,
+        generate_curves: bool = True,
+        use_fast_mimic: bool = True,
+        **kwargs: Any,
     ):
+        """
+        Initialize the MIMICRunner class with problem data and various experiment parameters.
+
+        Parameters
+        ----------
+        problem : Any
+            The optimization problem to be solved.
+        experiment_name : str
+            Name of the experiment.
+        seed : int
+            Random seed for reproducibility.
+        iteration_list : list of int
+            List of iterations for the experiment.
+        population_sizes : list of int
+            List of population sizes to test in the grid search.
+        keep_percent_list : list of float
+            List of keep percentages to test in the grid search.
+        max_attempts : int, optional
+            Maximum number of attempts without improvement before stopping.
+        generate_curves : bool, optional
+            Whether to generate learning curves.
+        use_fast_mimic : bool, optional
+            Whether to use the fast MIMIC mode, if available.
+        """
         super().__init__(
             problem=problem,
             experiment_name=experiment_name,
@@ -51,19 +94,38 @@ def __init__(
             generate_curves=generate_curves,
             **kwargs,
         )
-        self.keep_percent_list = keep_percent_list
-        self.population_sizes = population_sizes
-        self._use_fast_mimic = None
+        self.keep_percent_list: list[float] = keep_percent_list
+        self.population_sizes: list[int] = population_sizes
+        self._use_fast_mimic: bool | None = None
+
+        # Set fast MIMIC mode if available
         if hasattr(problem, "set_mimic_fast_mode") and callable(getattr(problem, "set_mimic_fast_mode")):
             self._use_fast_mimic = use_fast_mimic
             problem.set_mimic_fast_mode(use_fast_mimic)
 
     def _setup(self):
+        """
+        Perform any necessary setup before running the experiment.
+
+        Logs the current state of the fast MIMIC mode if it is enabled.
+        """
         super()._setup()
+
         if self._use_fast_mimic is not None:
             self.log_current_argument("use_fast_mimic", self._use_fast_mimic)
 
-    def run(self):
+    def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
+        """
+        Run the MIMIC algorithm experiment.
+
+        This method performs grid search over the provided population sizes
+        and keep percentages and returns the statistics and curves generated by the experiment.
+
+        Returns
+        -------
+        tuple
+            A tuple containing two DataFrames: run statistics and run curves
+        """
         return super().run_experiment(
             algorithm=mlrose_ky.mimic,
             pop_size=("Population Size", self.population_sizes),
diff --git a/src/mlrose_ky/runners/nngs_runner.py b/src/mlrose_ky/runners/nngs_runner.py
index c7bdfb0f..752ab464 100644
--- a/src/mlrose_ky/runners/nngs_runner.py
+++ b/src/mlrose_ky/runners/nngs_runner.py
@@ -77,7 +77,7 @@ def __init__(
         generate_curves: bool = True,
         output_directory: str = None,
         **kwargs: Any,
-    ) -> None:
+    ):
         """
         Initialize the NNGSRunner class with training and testing data and various experiment parameters.
 
@@ -119,10 +119,6 @@ def __init__(
             Whether to generate learning curves.
         output_directory : str, optional
             Directory to save output.
-
-        Returns
-        -------
-        None
         """
         # Take a copy of the grid search parameters
         grid_search_parameters = {**grid_search_parameters}
diff --git a/src/mlrose_ky/runners/rhc_runner.py b/src/mlrose_ky/runners/rhc_runner.py
index 62b0edc7..ad3ac06a 100644
--- a/src/mlrose_ky/runners/rhc_runner.py
+++ b/src/mlrose_ky/runners/rhc_runner.py
@@ -1,14 +1,6 @@
-"""Class for running optimization experiments using Random Hill Climbing (RHC), including grid search functionality."""
-
-# Authors: Andrew Rollings (modified by Kyle Nakamura)
-# License: BSD 3-clause
-
-import mlrose_ky
-from mlrose_ky.decorators import short_name
-
-from mlrose_ky.runners._runner_base import _RunnerBase
-
 """
+Class for running optimization experiments using Random Hill Climbing (RHC), including grid search functionality.
+
 Example usage:
 
     experiment_name = 'example_experiment'
@@ -20,17 +12,68 @@
                     seed=SEED,
                     iteration_list=2 ** np.arange(10),
                     max_attempts=5000,
-                    restart_list=[25, 75, 100])   
+                    restart_list=[25, 75, 100])
 
-    # the two data frames will contain the results
-    df_run_stats, df_run_curves = rhc.run()               
+    df_run_stats, df_run_curves = rhc.run()
 """
 
+# Authors: Andrew Rollings (modified by Kyle Nakamura)
+# License: BSD 3-clause
+
+from typing import Any
+
+import pandas as pd
+
+import mlrose_ky
+from mlrose_ky.decorators import short_name
+from mlrose_ky.runners._runner_base import _RunnerBase
+
 
 @short_name("rhc")
 class RHCRunner(_RunnerBase):
+    """
+    A runner for performing optimization experiments using Random Hill Climbing (RHC).
+
+    This class extends _RunnerBase and provides functionality for running experiments with the RHC algorithm,
+    including grid search over hyperparameters such as the number of restarts.
+
+    Attributes
+    ----------
+    restart_list : list[int]
+        List of restart values to test in the grid search.
+    """
 
-    def __init__(self, problem, experiment_name, seed, iteration_list, restart_list, max_attempts=500, generate_curves=True, **kwargs):
+    def __init__(
+        self,
+        problem: Any,
+        experiment_name: str,
+        seed: int,
+        iteration_list: list[int],
+        restart_list: list[int],
+        max_attempts: int = 500,
+        generate_curves: bool = True,
+        **kwargs: Any,
+    ):
+        """
+        Initialize the RHCRunner class with problem data and various experiment parameters.
+
+        Parameters
+        ----------
+        problem : Any
+            The optimization problem to be solved.
+        experiment_name : str
+            Name of the experiment.
+        seed : int
+            Random seed for reproducibility.
+        iteration_list : list of int
+            List of iterations for the experiment.
+        restart_list : list of int
+            List of restart values to test in the grid search.
+        max_attempts : int, optional
+            Maximum number of attempts without improvement before stopping.
+        generate_curves : bool, optional
+            Whether to generate learning curves.
+        """
         super().__init__(
             problem=problem,
             experiment_name=experiment_name,
@@ -40,7 +83,18 @@ def __init__(self, problem, experiment_name, seed, iteration_list, restart_list,
             generate_curves=generate_curves,
             **kwargs,
         )
-        self.restart_list = restart_list
+        self.restart_list: list[int] = restart_list
+
+    def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
+        """
+        Run the Random Hill Climbing (RHC) experiment.
+
+        This method performs grid search over the provided restart values and
+        returns the statistics and curves generated by the experiment.
 
-    def run(self):
+        Returns
+        -------
+        tuple
+            A tuple containing two DataFrames: run statistics and run curves.
+        """
         return super().run_experiment(algorithm=mlrose_ky.random_hill_climb, restarts=("Restarts", self.restart_list))
diff --git a/src/mlrose_ky/runners/sa_runner.py b/src/mlrose_ky/runners/sa_runner.py
index bf298463..a0fdf2f7 100644
--- a/src/mlrose_ky/runners/sa_runner.py
+++ b/src/mlrose_ky/runners/sa_runner.py
@@ -1,20 +1,11 @@
-"""Class for running optimization experiments using Simulated Annealing (SA), including grid search functionality."""
-
-# Authors: Andrew Rollings (modified by Kyle Nakamura)
-# License: BSD 3-clause
-
-import numpy as np
-
-import mlrose_ky
-from mlrose_ky.decorators import short_name
-from mlrose_ky.runners._runner_base import _RunnerBase
-
 """
+Class for running optimization experiments using Simulated Annealing (SA), including grid search functionality.
+
 Example usage:
 
     experiment_name = 'example_experiment'
     problem = TSPGenerator.generate(seed=SEED, number_of_cities=22)
-    
+
     # note that you can also initialize a temperature_list this way
     # temperature_list = [mlrose_ky.GeomDecay(init_temp=t, decay=d) for (t, d) in [(1, 0.99), (1e2, 0.999)]]
     # if you use this form, the decay_list parameter is ignored.
@@ -28,26 +19,75 @@
                   temperature_list=[1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],
                   decay_list=[mlrose_ky.GeomDecay])
 
-    # the two data frames will contain the results
-    df_run_stats, df_run_curves = sa.run()                  
+    df_run_stats, df_run_curves = sa.run()
 """
 
+# Authors: Andrew Rollings (modified by Kyle Nakamura)
+# License: BSD 3-clause
+
+from typing import Any, Callable
+
+import numpy as np
+import pandas as pd
+
+import mlrose_ky
+from mlrose_ky.decorators import short_name
+from mlrose_ky.runners._runner_base import _RunnerBase
+
 
 @short_name("sa")
 class SARunner(_RunnerBase):
+    """
+    A runner for performing optimization experiments using Simulated Annealing (SA).
+
+    This class extends _RunnerBase and provides functionality for running experiments
+    with the SA algorithm, including grid search over hyperparameters such as
+    temperature and decay schedules.
+
+    Attributes
+    ----------
+    temperature_list : list[float]
+        List of temperatures to test in the grid search.
+    decay_list : list[Callable], optional
+        List of decay schedules to use if raw temperature values are not provided.
+    use_raw_temp : bool
+        Whether to use raw temperature values or decay schedules.
+    """
 
     def __init__(
         self,
-        problem,
-        experiment_name,
-        seed,
-        iteration_list,
-        temperature_list,
-        decay_list=None,
-        max_attempts=500,
-        generate_curves=True,
-        **kwargs,
+        problem: Any,
+        experiment_name: str,
+        seed: int,
+        iteration_list: list[int],
+        temperature_list: list[float],
+        decay_list: list[Callable] | None = None,
+        max_attempts: int = 500,
+        generate_curves: bool = True,
+        **kwargs: dict,
     ):
+        """
+        Initialize the SARunner class with problem data and various experiment parameters.
+
+        Parameters
+        ----------
+        problem : Any
+            The optimization problem to be solved.
+        experiment_name : str
+            Name of the experiment.
+        seed : int
+            Random seed for reproducibility.
+        iteration_list : list of int
+            List of iterations for the experiment.
+        temperature_list : list of float
+            List of temperature values to test in the grid search.
+        decay_list : list of callable, optional
+            List of decay schedules to use, if raw temperature values are not provided.
+        max_attempts : int, optional
+            Maximum number of attempts without improvement before stopping.
+        generate_curves : bool, optional
+            Whether to generate learning curves.
+        """
         super().__init__(
             problem=problem,
             experiment_name=experiment_name,
@@ -58,14 +98,28 @@ def __init__(
             **kwargs,
         )
         self.use_raw_temp = True
-        self.temperature_list = temperature_list
+        self.temperature_list: list[float] = temperature_list
+
+        # Use decay schedules if provided
         if all([np.isscalar(x) for x in temperature_list]):
             if decay_list is None:
                 decay_list = [mlrose_ky.GeomDecay]
-            self.decay_list = decay_list
+
+            self.decay_list: list[Callable] = decay_list
             self.use_raw_temp = False
 
-    def run(self):
+    def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
+        """
+        Run the Simulated Annealing (SA) experiment.
+
+        This method performs grid search over the provided temperature values and
+        decay schedules, and returns the statistics and curves generated by the experiment.
+
+        Returns
+        -------
+        tuple
+            A tuple containing two DataFrames: run statistics and run curves.
+        """
         temperatures = (
             self.temperature_list if self.use_raw_temp else [d(init_temp=t) for t in self.temperature_list for d in self.decay_list]
         )
diff --git a/src/mlrose_ky/runners/skmlp_runner.py b/src/mlrose_ky/runners/skmlp_runner.py
index d11b0216..6c6bc397 100644
--- a/src/mlrose_ky/runners/skmlp_runner.py
+++ b/src/mlrose_ky/runners/skmlp_runner.py
@@ -43,7 +43,7 @@ class _MLPClassifier(BaseEstimator):
             Additional keyword arguments to be passed to MLPClassifier.
         """
 
-        def __init__(self, runner: "SKMLPRunner", **kwargs) -> None:
+        def __init__(self, runner: "SKMLPRunner", **kwargs):
             """
             Initialize the _MLPClassifier wrapper.
 
@@ -87,7 +87,7 @@ def __getattr__(self, item: str, default: Any = None) -> Any:
 
             return self.__dict__[item] if item in self.__dict__ else default
 
-        def __setattr__(self, item: str, value: Any) -> None:
+        def __setattr__(self, item: str, value: Any):
             """Set an attribute on the MLPClassifier if it exists, otherwise set it on the class instance."""
             if "mlp" in self.__dict__ and hasattr(self.__dict__["mlp"], item):
                 self.__dict__["mlp"].__setattr__(item, value)
@@ -243,7 +243,7 @@ def _loss_grad_lbfgs_intercept(
 
             return f, g
 
-        def _invoke_runner_callback(self) -> None:
+        def _invoke_runner_callback(self):
             """Invoke the runner callback to save the current state of training."""
             # noinspection PyProtectedMember
             no_improvement_count = self.mlp._no_improvement_count if hasattr(self.mlp, "_no_improvement_count") else 0
@@ -290,7 +290,7 @@ def __init__(
         output_directory: str = None,
         replay: bool = False,
         **kwargs: dict,
-    ) -> None:
+    ):
         """
         Initialize the SKMLPRunner class with training and testing data and various experiment parameters.
 
@@ -330,10 +330,6 @@ def __init__(
             Directory to save output.
         replay : bool, optional
             Whether to replay the experiment.
-
-        Returns
-        -------
-        None
         """
         grid_search_parameters = {**grid_search_parameters}
 
diff --git a/src/mlrose_ky/samples/synthetic_data.py b/src/mlrose_ky/samples/synthetic_data.py
index efa677b1..009bacd1 100644
--- a/src/mlrose_ky/samples/synthetic_data.py
+++ b/src/mlrose_ky/samples/synthetic_data.py
@@ -205,7 +205,7 @@ def plot_synthetic_dataset(
     classifier: Any = None,
     transparent_bg: bool = False,
     bg_color: str = "white",
-) -> None:
+):
     """
     Plot the synthetic dataset.