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Commitfaeec77

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PyGAD 2.16.3
PyGAD 2.16.21. A new instance attribute called `previous_generation_fitness` added in the `pygad.GA` class. It holds the fitness values of one generation before the fitness values saved in the `last_generation_fitness`.2. Issue in the `cal_pop_fitness()` method in getting the correct indices of the previous parents. This is solved by using the previous generation's fitness saved in the new attribute `previous_generation_fitness` to return the parents' fitness values. Thanks to Tobias Tischhauser (M.Sc. - [Mitarbeiter Institut EMS, Departement Technik, OST – Ostschweizer Fachhochschule, Switzerland](https://www.ost.ch/de/forschung-und-dienstleistungen/technik/systemtechnik/ems/team)) for detecting this bug.PyGAD 2.16.31. Validate the fitness value returned from the fitness function. An exception is raised if something is wrong.ahmedfgad#67
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‎__init__.py

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Original file line numberDiff line numberDiff line change
@@ -1,3 +1,3 @@
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from .pygadimport*# Relative import.
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3-
__version__="2.16.1"
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__version__="2.16.3"

‎pygad.py

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Original file line numberDiff line numberDiff line change
@@ -899,6 +899,7 @@ def __init__(self,
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self.last_generation_parents=None# A list holding the parents of the last generation.
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self.last_generation_offspring_crossover=None# A list holding the offspring after applying crossover in the last generation.
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self.last_generation_offspring_mutation=None# A list holding the offspring after applying mutation in the last generation.
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self.previous_generation_fitness=None# Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.26.2
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defround_genes(self,solutions):
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forgene_idxinrange(self.num_genes):
@@ -1146,16 +1147,20 @@ def cal_pop_fitness(self):
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# Calculating the fitness value of each solution in the current population.
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forsol_idx,solinenumerate(self.population):
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# Check iftheparent'sfitness value is already calculated. If so, useit instead of calling the fitness function.
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ifnot(self.last_generation_parentsisNone)andlen(numpy.where(numpy.all(self.last_generation_parents==sol,axis=1))[0]>0):
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# Check ifthis solution is aparent from the previous generation and itsfitness value is already calculated. If so, usethe fitness value instead of calling the fitness function.
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if (self.last_generation_parentsisnotNone)andlen(numpy.where(numpy.all(self.last_generation_parents==sol,axis=1))[0]>0):
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# Index of the parent in the parents array (self.last_generation_parents). This is not its index within the population.
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parent_idx=numpy.where(numpy.all(self.last_generation_parents==sol,axis=1))[0][0]
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# Index of the parent in the population.
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parent_idx=self.last_generation_parents_indices[parent_idx]
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# Use the parent's index to return its pre-calculated fitness value.
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fitness=self.last_generation_fitness[parent_idx]
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fitness=self.previous_generation_fitness[parent_idx]
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else:
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fitness=self.fitness_func(sol,sol_idx)
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iftype(fitness)inGA.supported_int_float_types:
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pass
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else:
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raiseValueError("The fitness function should return a number but the value {fit_val} of type {fit_type} found.".format(fit_val=fitness,fit_type=type(fitness)))
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pop_fitness.append(fitness)
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pop_fitness=numpy.array(pop_fitness)
@@ -1244,6 +1249,7 @@ def run(self):
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ifnot (self.on_mutationisNone):
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self.on_mutation(self,self.last_generation_offspring_mutation)
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# Update the population attribute according to the offspring generated.
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if (self.keep_parents==0):
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self.population=self.last_generation_offspring_mutation
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elif (self.keep_parents==-1):
@@ -1257,6 +1263,7 @@ def run(self):
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self.generations_completed=generation+1# The generations_completed attribute holds the number of the last completed generation.
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self.previous_generation_fitness=self.last_generation_fitness.copy()
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# Measuring the fitness of each chromosome in the population. Save the fitness in the last_generation_fitness attribute.
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self.last_generation_fitness=self.cal_pop_fitness()
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