gene_space example

Author: ahmedfgad

examples/example_gene_space.py

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+"""
+PyGAD Example: Using the gene_space Parameter
+==============================================
+
+The `gene_space` parameter controls the set of valid values that each gene
+can take. This example demonstrates all major forms of `gene_space`.
+
+Documentation:
+  https://pygad.readthedocs.io/en/latest/pygad_more.html#more-about-the-gene-space-parameter
+
+Dependencies:
+  pip install pygad numpy
+"""
+
+import numpy
+import pygad
+
+
+def fitness_func(ga_instance, solution, solution_idx):
+    """Return the sum of all gene values as the fitness score."""
+    return numpy.sum(solution)
+
+
+# ===========================================================================
+# 1. Single flat list or NumPy array (shared across all genes)
+# ===========================================================================
+# Every gene independently draws its value from the same list.
+# Using None in the list means that gene value is unconstrained (random float).
+
+# gene_space = [1, 2, 3, 4, 5]
+# gene_space = [1, 2, None]
+# gene_space = numpy.array([10, 20, 30, 40, 50])
+
+# ===========================================================================
+# 2. A range or NumPy sequence (shared across all genes)
+# ===========================================================================
+
+# gene_space = range(1, 11)                        # integers 1 through 10
+# gene_space = numpy.arange(0.0, 1.1, 0.1)         # [0.0, 0.1, ..., 1.0]
+# gene_space = numpy.linspace(1, 5, num=9)          # 9 evenly-spaced floats in [1, 5]
+
+# ===========================================================================
+# 3. Per-gene list (each gene has its own independent space)
+# ===========================================================================
+# When gene_space is a list with length equal to num_genes, each element
+# defines the space for the corresponding gene. Elements can be a list,
+# a NumPy array, a range, or None (unconstrained).
+
+# gene_space = [range(1, 5), range(5, 11), range(10, 21)]
+# gene_space = [[1, 2, 3], None, range(10, 21)]
+# gene_space = [[1, 2, 3], numpy.linspace(5, 10, num=6), [10, 12, 14, 16, 18, 20]]
+
+# ===========================================================================
+# 4. Dictionary – continuous range (shared across all genes)
+# ===========================================================================
+# A dict with "low" and "high" makes every gene sample from a continuous
+# uniform distribution. An optional "step" key discretises the range.
+
+# gene_space = {"low": 0.0, "high": 10.0}
+# gene_space = {"low": 0.0, "high": 10.0, "step": 0.5}
+
+# ===========================================================================
+# 5. Per-gene list of dictionaries (each gene has its own continuous range)
+# ===========================================================================
+# Each gene gets its own dict-based range. This is the most expressive form
+# and is useful when each gene lives in a different numerical domain.
+
+gene_space = [
+    [1, 2, 3, 4, 5], # gene 0
+    {"low": 5,  "high": 10}, # gene 1
+    range(10, 15), # gene 2
+    None, # gene 3
+]
+
+ga_instance = pygad.GA(
+    num_generations=50,
+    num_parents_mating=4,
+    sol_per_pop=8,
+    num_genes=4,
+    fitness_func=fitness_func,
+    gene_space=gene_space,
+)
+
+ga_instance.run()
+
+best_solution, best_fitness, _ = ga_instance.best_solution()
+print(f"Best solution : {best_solution}")
+print(f"Best fitness  : {best_fitness:.4f}")