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Commit fb6cdc4

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Filter parent selection type
1 parent 0e8be9d commit fb6cdc4

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‎pygad/pygad.py

Lines changed: 12 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1135,9 +1135,8 @@ def __init__(self,
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# Validate delay_after_gen
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if type(delay_after_gen) in GA.supported_int_float_types:
1138-
if delay_after_gen != 0.0:
1139-
if not self.suppress_warnings:
1140-
warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
1138+
if not self.suppress_warnings:
1139+
warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
11411140
if delay_after_gen >= 0.0:
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self.delay_after_gen = delay_after_gen
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else:
@@ -1914,6 +1913,16 @@ def run(self):
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# Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute.
19151914
self.last_generation_fitness = self.cal_pop_fitness()
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1916+
# Know whether the problem is SOO or MOO.
1917+
if type(self.last_generation_fitness[0]) in GA.supported_int_float_types:
1918+
# Single-objective problem.
1919+
# If the problem is SOO, the parent selection type cannot be nsga2 or tournament_nsga2.
1920+
if self.parent_selection_type in ['nsga2', 'tournament_nsga2']:
1921+
raise TypeError(f"Incorrect parent selection type. The fitness function returned a single numeric fitness value which means the problem is single-objective. But the parent selection type {self.parent_selection_type} is used which only works for multi-objective optimization problems.")
1922+
elif type(self.last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
1923+
# Multi-objective problem.
1924+
pass
1925+
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best_solution, best_solution_fitness, best_match_idx = self.best_solution(pop_fitness=self.last_generation_fitness)
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# Appending the best solution in the initial population to the best_solutions list.

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