Refactor GeneticAlgorithm imports and formatting

Updated import statements for clarity and consistency, including explicit imports from metazoo.bio.utils. Improved code formatting and spacing, and fixed minor style issues such as commented code alignment and line breaks.

Author: roicort

metazoo/src/metazoo/bio/evolutionary/ga.py

@@ -1,17 +1,19 @@
-# Genetic Algorithms
+# Genetic Algorithm (GA)
+
+from typing import Callable, Optional, Sequence, Tuple
 
-from typing import Optional, Callable, Sequence, Tuple
 import numpy as np
+from plotly import express as px
+from rich.console import Console
 from rich.progress import Progress
 from rich.table import Table
-from rich.console import Console
-from plotly import express as px
 
-from .utils import Population, Encoding
+from metazoo.bio.utils import Encoding, Population
 
 
 class GeneticAlgorithm:
     """A Simple Genetic Algorithm (GA) implementation."""
+
     def __init__(
         self,
         fitness_function: Callable[[np.ndarray], float],
@@ -54,16 +56,16 @@ def summary(self):
         table.add_row("Genome Length", str(self.genome_length))
         table.add_row("Mutation Rate", str(self.mutation_rate))
         table.add_row("Crossover Rate", str(self.crossover_rate))
-        #table.add_row("Encoding", str(self.encoding))
+        # table.add_row("Encoding", str(self.encoding))
         table.add_row("Selection Function", self.selection_function.__name__)
         table.add_row("Crossover Function", self.crossover_function.__name__)
         table.add_row("Mutation Function", self.mutation_function.__name__)
         table.add_row("Fitness Function", self.fitness_function.__name__)
-        #table.add_row("Dimension", str(self.dim))
+        # table.add_row("Dimension", str(self.dim))
         table.add_row(
             "Elitism", str(self.elitism) if self.elitism is not None else "None"
         )
-        #if self.encoding == "binary":
+        # if self.encoding == "binary":
         #    table.add_row("Epsilon", str(self.epsilon))
         #    table.add_row("Bits Per Var", str(self.bits_per_var))
         #    table.add_row("Genome Length", str(self.genome_length))
@@ -76,7 +78,7 @@ def eval(self):
         """
         Evaluate the fitness of the current population.
         """
-        
+
         # Raw Fitness.
 
         raw_fitness = np.array(
@@ -109,7 +111,9 @@ def evolve(self):
         fitness, fitness_transformed = self.eval()
         self.fitness_history.append(fitness.mean())
         self.best_history.append(self.best_fitness)
-        selected_indices = self.selection_function(self.population.individuals, fitness_transformed)
+        selected_indices = self.selection_function(
+            self.population.individuals, fitness_transformed
+        )
         selected_parents = self.population.individuals[selected_indices]
         next_generation = self.create_descendants(selected_parents)
 
@@ -177,7 +181,10 @@ def run(
                 pop_history.append(self.population.individuals.copy())
                 best_history.append(self.best_individual)
         if history:
-            pop_history = [[self.population.encoding.decode(ind) for ind in gen] for gen in pop_history]
+            pop_history = [
+                [self.population.encoding.decode(ind) for ind in gen]
+                for gen in pop_history
+            ]
             return (best_history, pop_history)
 
     def fitness_plot(self, best=False) -> None:
@@ -195,6 +202,6 @@ def fitness_plot(self, best=False) -> None:
                     labels={"x": "Generation", "y": "Best Fitness"},
                     title="Best Fitness History",
                 )
-                return fig 
+                return fig
         else:
             raise ValueError("No fitness history to plot. Run the algorithm first.")