when set mutation_type="adaptive", it ran into error.

[DailiZhang2010]

[ahmedfgad]

Hi @DailiZhang2010,

The problem is related to the fitness array. I cannot figure out the problem from the screenshot. Is it possible to share the code? You are welcome to send it by email (ahmed.f.gadd@gmail.com) if this is better for you.

[DailiZhang2010]

Hi Ahmed, Thanks a lot for the response. For the actual application I am developing, it involves quite some data reading, so I copied the sample on PyGAD doc site, but changed two lines. Please see the attached file. mutation_type = "adaptive" mutation_num_genes=(3, 1) It ran to the same error. Please see the attached file. Best regards, Daili [image: error_log.PNG]

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On Tue, Sep 28, 2021 at 12:23 PM Ahmed Gad ***@***.***> wrote: Hi @DailiZhang2010 <https://github.com/DailiZhang2010>, The problem is related to the fitness array. I cannot figure out the problem from the screenshot. Is it possible to share the code? You are welcome to send it by email ***@***.***) if this is better for you. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#65 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AHPLYELU25PBTGWOI7FHG2TUEH2ZXANCNFSM5ESIPFOA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[ahmedfgad]

I already tested the sample code posted in the documentation with the following 2 lines and everything works well.

mutation_type = "adaptive"
mutation_num_genes=(3, 1)

The problem exists in the fitness function. For the error case, could you check if the fitness function returns a single number?

[DailiZhang2010]

hmm, thanks. I guess the .py file was not attached. Here is the example I used to test the adaptive mutation_type. Regards, Daili

On Tue, Sep 28, 2021 at 4:59 PM Ahmed Gad ***@***.***> wrote: I already tested the sample code posted in the documentation with the following 2 lines and everything works well. mutation_type = "adaptive"mutation_num_genes=(3, 1) The problem exists in the fitness function. For the error case, could you check if the fitness function returns a single number? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#65 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AHPLYEKJIFRXVT2SCUXHBP3UEI3EFANCNFSM5ESIPFOA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

import numpy import pygad function_inputs = [4,-2,3.5,5,-11,-4.7] desired_output = 44 def fitness_func(solution, solution_idx): output = numpy.sum(solution*function_inputs) fitness = 1.0 / numpy.abs(output - desired_output) return fitness fitness_function = fitness_func num_generations = 50 num_parents_mating = 4 sol_per_pop = 8 num_genes = len(function_inputs) init_range_low = -2 init_range_high = 5 parent_selection_type = "sss" keep_parents = 1 crossover_type = "single_point" mutation_type = "adaptive" mutation_num_genes=(3, 1) # mutation_type = "random" # mutation_percent_genes = 10 ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, fitness_func=fitness_function, sol_per_pop=sol_per_pop, num_genes=num_genes, init_range_low=init_range_low, init_range_high=init_range_high, parent_selection_type=parent_selection_type, keep_parents=keep_parents, crossover_type=crossover_type, mutation_type=mutation_type, mutation_num_genes=mutation_num_genes) ga_instance.run() solution, solution_fitness, solution_idx = ga_instance.best_solution() print("Parameters of the best solution : {solution}".format(solution=solution)) print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness)) prediction = numpy.sum(numpy.array(function_inputs)*solution) print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))

[DailiZhang2010]

Ahmed, I forgot to mention that if I change the mutation_type="random", it works well. And the error message basically says the # of offspring is more than the ( sol_per_pop-num_parents_mating) . Thanks, Daili On Tue, Sep 28, 2021 at 5:18 PM Daili Zhang ***@***.***> wrote:

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hmm, thanks. I guess the .py file was not attached. Here is the example I used to test the adaptive mutation_type. Regards, Daili On Tue, Sep 28, 2021 at 4:59 PM Ahmed Gad ***@***.***> wrote: > I already tested the sample code posted in the documentation with the > following 2 lines and everything works well. > > mutation_type = "adaptive"mutation_num_genes=(3, 1) > > The problem exists in the fitness function. For the error case, could you > check if the fitness function returns a single number? > > — > You are receiving this because you were mentioned. > Reply to this email directly, view it on GitHub > <#65 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AHPLYEKJIFRXVT2SCUXHBP3UEI3EFANCNFSM5ESIPFOA> > . > Triage notifications on the go with GitHub Mobile for iOS > <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> > or Android > <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. > >

[ahmedfgad]

@DailiZhang2010,

Thanks for opening this issue. I figured out where is the problem which is related to the keep_parents parameter. When all parents are used in the next population (i.e. keep_parents=-1), things work well. When the number of kept parents is less than the number of mating parents, the problem arises.

It is solved now.

The project will be updated soon and a new release of PyGAD will be published. Please have a try when the new release is published and get back if you found any bugs.

[DailiZhang2010]

Cool. Thanks a lot for such an awesome package and it helped my project a lot. Regards, Daili

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On Tue, Sep 28, 2021, 7:00 PM Ahmed Gad ***@***.***> wrote: @DailiZhang2010 <https://github.com/DailiZhang2010>, Thanks for opening this issue. I figured out where is the problem. It is solved now. The project will be updated soon and a new release of PyGAD will be published. Please have a try when the new release is published and get back if you found any bugs. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#65 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AHPLYELTP3KLV7NP2W2UDHLUEJJI3ANCNFSM5ESIPFOA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.