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docs/source/Footer.rst

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@@ -845,7 +845,7 @@ Release Date: 28 September 2021
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``save_solutions=True``.
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2. The user can use the ``tqdm`` library to show a progress bar.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50
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https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/50.
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.. code:: python
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@@ -874,6 +874,50 @@ Release Date: 28 September 2021
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ga_instance.plot_result()
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But this work does not work if the ``ga_instance`` will be pickled (i.e.
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the ``save()`` method will be called.
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.. code:: python
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ga_instance.save("test")
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To solve this issue, define a function and pass it to the
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``on_generation`` parameter. In the next code, the
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``on_generation_progress()`` function is defined which updates the
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progress bar.
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.. code:: python
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import pygad
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import numpy
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import tqdm
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equation_inputs = [4,-2,3.5]
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desired_output = 44
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def fitness_func(solution, solution_idx):
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output = numpy.sum(solution * equation_inputs)
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fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
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return fitness
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def on_generation_progress(ga):
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pbar.update(1)
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num_generations = 100
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with tqdm.tqdm(total=num_generations) as pbar:
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ga_instance = pygad.GA(num_generations=num_generations,
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sol_per_pop=5,
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num_parents_mating=2,
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num_genes=len(equation_inputs),
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fitness_func=fitness_func,
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on_generation=on_generation_progress)
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ga_instance.run()
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ga_instance.plot_result()
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ga_instance.save("test")
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1. Solved the issue of unequal length between the ``solutions`` and
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``solutions_fitness`` when the ``save_solutions`` parameter is set to
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``True``. Now, the fitness of the last population is appended to the
@@ -1366,8 +1410,8 @@ A number of research papers used PyGAD and here are some of them:
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- Jaros, Marta, and Jiri Jaros. "Performance-Cost Optimization of
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Moldable Scientific Workflows."
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- Thorat, Divya. *Enhanced genetic algorithm to reduce makespan of
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multiple jobs in map-reduce application on serverless platform*.
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- Thorat, Divya. "Enhanced genetic algorithm to reduce makespan of
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multiple jobs in map-reduce application on serverless platform".
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Diss. Dublin, National College of Ireland, 2020.
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- Koch, Chris, and Edgar Dobriban. "AttenGen: Generating Live
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Long Short-Term Memory Network for Long-Term Load Forecasting." *IEEE
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Access* 9 (2021): 68511-68522.
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- Antunes, E. D. O., Caetano, M. F., Marotta, M. A., Araujo, A.,
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Bondan, L., Meneguette, R. I., & Rocha Filho, G. P. (2021, August).
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Soluções Otimizadas para o Problema de Localização de Máxima
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Cobertura em Redes Militarizadas 4G/LTE. In *Anais do XXVI Workshop
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de Gerência e Operação de Redes e Serviços* (pp. 152-165). SBC.
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More Links
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==========
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https://rodriguezanton.com/identifying-contact-states-for-2d-objects-using-pygad-and/
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https://torvaney.github.io/projects/t9-optimised
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For More Information
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====================
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