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Last change
on this file was
1290,
checked in by Maciej Komosinski, 16 months ago
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Better mutation, crossover, and evaluation function for a simple minimalistic numerical optimization example
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File size:
609 bytes
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| 1 | import numpy as np |
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| 2 | |
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| 3 | from ..base.experiment_abc import ExperimentABC |
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| 4 | |
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| 5 | |
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| 6 | class ExperimentNumerical(ExperimentABC): |
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| 7 | def __init__(self, hof_size, popsize, save_only_best) -> None: |
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| 8 | ExperimentABC.__init__(self,popsize=popsize, |
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| 9 | hof_size=hof_size, |
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| 10 | save_only_best=save_only_best) |
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| 11 | |
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| 12 | def mutate(self, gen): |
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| 13 | return gen + np.random.normal(0, 15, len(gen)) |
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| 14 | |
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| 15 | def cross_over(self, gen1, gen2): |
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| 16 | a = np.random.uniform() |
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| 17 | return a * gen1 + (1.0-a) * gen2 |
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| 18 | |
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| 19 | def evaluate(self, gen): |
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| 20 | return -sum([x*x for x in gen]) |
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