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Last change
on this file since 1236 was
1190,
checked in by Maciej Komosinski, 2 years ago
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Added the "evolalg" module for evolutionary optimization
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File size:
1.1 KB
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| 1 | from .numerical_example.numerical_example import ExperimentNumerical
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| 2 |
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| 3 |
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| 4 | def main():
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| 5 | parsed_args = ExperimentNumerical.get_args_for_parser().parse_args()
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| 6 | print("Argument values:", ", ".join(
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| 7 | ['%s=%s' % (arg, getattr(parsed_args, arg)) for arg in vars(parsed_args)]))
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| 8 |
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| 9 | initialgenotype = [100, 100, 100, 100]
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| 10 | print('Best individuals:')
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| 11 | experiment = ExperimentNumerical(
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| 12 | hof_size=parsed_args.hof_size,
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| 13 | popsize=parsed_args.popsize,
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| 14 | save_only_best=parsed_args.save_only_best)
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| 15 |
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| 16 | hof, stats = experiment.evolve(hof_savefile=parsed_args.hof_savefile,
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| 17 | generations=parsed_args.generations,
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| 18 | initialgenotype=initialgenotype,
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| 19 | pmut=parsed_args.pmut,
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| 20 | pxov=parsed_args.pxov,
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| 21 | tournament_size=parsed_args.tournament)
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| 22 | print(len(hof))
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| 23 | for ind in hof:
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| 24 | print(ind.genotype, ind.rawfitness)
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| 25 |
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| 26 |
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| 27 | if __name__ == "__main__":
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| 28 | main()
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