Merge pull request #46 from jbytecode/main

introduce OptimizationMethod to enumerate cga, mcccga, asyncga, etc.

src/optimizer.py

@@ -110,7 +110,7 @@ def cga(
     best_objectives = []
     best_ever_solution = []
     avg_objectives = []
-    method_name = "cga"
+    method_name = OptimizationMethod.CGA
 
     # Generate Initial Population
     pop_list = Population(method_name, ch_size, n_rows, n_cols,
@@ -250,7 +250,7 @@ def sync_cga(
     best_objectives = []
     best_ever_solution = []
     avg_objectives = []
-    method_name = "sync_cga"
+    method_name = OptimizationMethod.SYNCGA
 
 
     # Generate Initial Population
@@ -395,7 +395,7 @@ def alpha_cga(
     best_objectives = []
     best_ever_solution = []
     avg_objectives = []
-    method_name = "alpha_cga"
+    method_name = OptimizationMethod.ALPHA_CGA
 
 
     # Generate Initial Population
@@ -547,7 +547,7 @@ def ccga(
     best_ever_solution = []
     avg_objectives = []
     vector = [0.5 for _ in range(ch_size)]
-    method_name = "ccga"
+    method_name = OptimizationMethod.CCGA
 
 
     # Generate Initial Population
@@ -657,7 +657,7 @@ def mcccga(
     best_objectives = []
     best_ever_solution = []
     avg_objectives = []
-    method_name = "mcccga"
+    method_name = OptimizationMethod.MCCCGA
 
     # Generate initial probability vector
     vector = generate_probability_vector(mins, maxs, pop_size)

src/population.py

@@ -2,11 +2,18 @@
 from individual import *
 from grid import *
 from neighborhoods.linear_9 import Linear9
-
 from byte_operators import *
-
 from problems.abstract_problem import AbstractProblem
+from enum import Enum 
+
 
+# "cga", "sync_cga", "alpha_cga", "ccga", "mcccga"
+class OptimizationMethod(Enum):
+    CGA = 1
+    SYNCGA = 2
+    ALPHA_CGA = 3
+    CCGA = 4
+    MCCCGA = 5
 
 
 class Population:
@@ -15,6 +22,8 @@ class Population:
 
     Attributes
     ----------
+    method_name : OptimizationMethod
+        The name of the optimization method. Must be one of OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA, or OptimizationMethod.MCCCGA.
     ch_size : int
         The size of the chromosome.
     n_rows : int
@@ -29,7 +38,7 @@ class Population:
         A list used to generate candidates for the population (relevant for MCCCGA).
     """
     def __init__(self, 
-                 method_name: str = "", 
+                 method_name: OptimizationMethod = OptimizationMethod.CGA, 
                  ch_size: int = 0, 
                  n_rows: int = 0, 
                  n_cols: int = 0, 
@@ -43,6 +52,9 @@ def __init__(self,
 
         Parameters
         ----------
+        method_name : OptimizationMethod.
+            The name of the optimization method. Must be one of OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA, or OptimizationMethod.MCCCGA.
+            Default is OptimizationMethod.CGA.
         ch_size : int, optional
             The size of the chromosome (default is 0).
         n_rows : int, optional
@@ -89,12 +101,12 @@ def initial_population(self) -> List[Individual]:
                              mins = self.mins, maxs = self.maxs)
 
             # Initialize chromosome and evaluate fitness for cga, syn_cga and alpha_cga
-            if self.method_name in ["cga", "sync_cga", "alpha_cga", "ccga"]:
+            if self.method_name in [OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA]:
                 ind.chromosome = ind.randomize()
                 ind.fitness_value = self.problem.f(ind.chromosome)
 
             # Initialize chromosome and evaluate fitness for cga and mcccga
-            elif self.method_name in ["mcccga"]:
+            elif self.method_name in [OptimizationMethod.MCCCGA]:
                 ind.chromosome = ind.generate_candidate(self.vector)
                 ind_byte_ch = bits_to_floats(ind.chromosome)
                 ind.fitness_value = self.problem.f(ind_byte_ch)

src/tests/test_population.py

@@ -5,7 +5,7 @@
 from byte_operators import bits_to_floats
 from problems.abstract_problem import AbstractProblem
 from typing import List
-from population import Population
+from population import Population, OptimizationMethod
 
 
 class MockProblem(AbstractProblem):
@@ -34,7 +34,7 @@ def setup_population():
     """
     mock_problem = MockProblem()
     return Population(
-        method_name="cga",
+        method_name=OptimizationMethod.CGA,
         ch_size=10,
         n_rows=3,
         n_cols=3,

src/tests/test_roulette_wheel_selection.py

@@ -1,6 +1,6 @@
 from problems.single_objective.discrete.binary.one_max import OneMax
 from selection.roulette_wheel_selection import RouletteWheelSelection
-from population import Population
+from population import Population, OptimizationMethod
 from individual import GeneType
 
 def test_roulette_wheel_selection():
@@ -29,7 +29,7 @@ def test_roulette_wheel_selection():
     c = 0
 
     # Initialize the population
-    pop_list = Population("cga", CH_SIZE, N_ROWS, N_COLS, GEN_TYPE, problem).initial_population()
+    pop_list = Population(OptimizationMethod.CGA, CH_SIZE, N_ROWS, N_COLS, GEN_TYPE, problem).initial_population()
 
     # Perform roulette wheel selection to get parent individuals
     parents = RouletteWheelSelection(pop_list, c).get_parents()

src/tests/test_tournament_selection.py

@@ -1,6 +1,6 @@
 from problems.single_objective.discrete.binary.one_max import OneMax
 from selection.tournament_selection import TournamentSelection
-from population import Population
+from population import Population, OptimizationMethod
 from individual import GeneType
 
 def test_tournament_selection():
@@ -34,7 +34,7 @@ def test_tournament_selection():
     c = 0
 
     # Initialize the population
-    pop_list = Population("cga",CH_SIZE, N_ROWS, N_COLS, GEN_TYPE, problem).initial_population()
+    pop_list = Population(OptimizationMethod.CGA,CH_SIZE, N_ROWS, N_COLS, GEN_TYPE, problem).initial_population()
 
     # Perform tournament selection to get parent individuals
     parents = TournamentSelection(pop_list, c, K_TOURNAMENT).get_parents()