Comments (4)
from geneticalgorithmpython.
Hi Keith,
Thanks for response. I did not quite understood, why 640/40 would give 15?
Yes, you are right i have got 15 genes and 40 competing algos in a generation.
Regardless if it gives 600 or 640, my expectation would be that number of iterated solutions would be equal to number of fitness values. Sorry, if i am saying something non-sensible.
Regards,
Javid
from geneticalgorithmpython.
from geneticalgorithmpython.
Hi @javid-b,
Thanks for opening this issue. You are right as the fitness of the last population was not saved in the solutions_fitness
list. This is solved by adding the next code at the end of the run()
method.
if self.save_solutions:
self.solutions_fitness.extend(self.last_generation_fitness)
The project will be updated soon and a new release of PyGAD will be published too.
Please let me know if you have any bugs or enhancements.
from geneticalgorithmpython.
Related Issues (20)
- Multi-Objective Optimization and parallel_processing
- Exceptions when running multi-objective problems HOT 1
- Save the instance of a GA during fitness evaluation HOT 1
- Error when run parallel_processing "process" HOT 3
- multi objective optimization - metric of quality HOT 1
- Force recalculation of fitness values for each new generation ? HOT 7
- Fitness value of a solution computed multiple times per generation? HOT 5
- The on_generation callback is not called at the end of generation 0 HOT 2
- GA
- Convergence issue: good solution appears to be forgotten, resulting best solution is not even valid HOT 1
- Error when running 3.2.0 vs. 3.1.0 for ga_instance.best_solutions
- ga_instance.save will not work with tqdm example HOT 1
- fitness function is being saved? HOT 2
- Cannot modify the attributes of the ga_instance when running multiple processes in parallel HOT 1
- Manipulate solution before saving it as parent HOT 5
- `pareto_fronts` is from previous generation HOT 1
- `initial_population` not effectively used/retained for multiobjective problems? HOT 4
- Training traffic sign recognition with faster rcnn using ga HOT 1
- ga_instance.best_solution() does not return the solution that minimizes my fitness function HOT 1
- delay_after_gen warning HOT 1
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