8000 Bug fix when gene_type is nested. · nuhash/GeneticAlgorithmPython@788423d · GitHub
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ahmedfgadahmedfgad
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Bug fix when gene_type is nested.
Fix some bugs when the gene_type parameter is nested. Thanks to Rainer for opening a discussion (ahmedfgad#43) to report this bug: ahmedfgad#43 (reply in thread)
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‎pygad.py

Lines changed: 17 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1578,12 +1578,16 @@ def mutation_randomly(self, offspring):
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random_value = self.gene_type(random_value)
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else:
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random_value = self.gene_type[gene_idx](random_value)
1581-
# If the mutation_by_replacement attribute is False, then the random value is added to the gene value.
1581+
if type(random_value) is numpy.ndarray:
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random_value = random_value[0]
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# If the mutation_by_replacement attribute is False, then the random value is added to the gene value.
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else:
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if self.gene_type_single == True:
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random_value = self.gene_type(offspring[offspring_idx, gene_idx] + random_value)
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else:
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random_value = self.gene_type[gene_idx](offspring[offspring_idx, gene_idx] + random_value)
1589+
if type(random_value) is numpy.ndarray:
1590+
random_value = random_value[0]
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offspring[offspring_idx, gene_idx] = random_value
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@@ -1621,12 +1625,16 @@ def mutation_probs_randomly(self, offspring):
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random_value = self.gene_type(random_value)
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else:
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random_value = self.gene_type[gene_idx](random_value)
1628+
if type(random_value) is numpy.ndarray:
1629+
random_value = random_value[0]
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# If the mutation_by_replacement attribute is False, then the random value is added to the gene value.
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else:
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if self.gene_type_single == True:
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random_value = self.gene_type(offspring[offspring_idx, gene_idx] + random_value)
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else:
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random_value = self.gene_type[gene_idx](offspring[offspring_idx, gene_idx] + random_value)
1636+
if type(random_value) is numpy.ndarray:
1637+
random_value = random_value[0]
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offspring[offspring_idx, gene_idx] = random_value
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@@ -1872,12 +1880,16 @@ def adaptive_mutation_randomly(self, offspring):
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random_value = self.gene_type(random_value)
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else:
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random_value = self.gene_type[gene_idx](random_value)
1883+
if type(random_value) is numpy.ndarray:
1884+
random_value = random_value[0]
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# If the mutation_by_replacement attribute is False, then the random value is added to the gene value.
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else:
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if self.gene_type_single == True:
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random_value = self.gene_type(offspring[offspring_idx, gene_idx] + random_value)
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else:
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random_value = self.gene_type[gene_idx](offspring[offspring_idx, gene_idx] + random_value)
1891+
if type(random_value) is numpy.ndarray:
1892+
random_value = random_value[0]
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offspring[offspring_idx, gene_idx] = random_value
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@@ -2011,12 +2023,16 @@ def adaptive_mutation_probs_randomly(self, offspring):
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random_value = self.gene_type(random_value)
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else:
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random_value = self.gene_type[gene_idx](random_value)
2026+
if type(random_value) is numpy.ndarray:
2027+
random_value = random_value[0]
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# If the mutation_by_replacement attribute is False, then the random value is added to the gene value.
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else:
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if self.gene_type_single == True:
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random_value = self.gene_type(offspring[offspring_idx, gene_idx] + random_value)
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else:
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random_value = self.gene_type[gene_idx](offspring[offspring_idx, gene_idx] + random_value)
2034+
if type(random_value) is numpy.ndarray:
2035+
random_value = random_value[0]
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20212037
offspring[offspring_idx, gene_idx] = random_value
20222038

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