A histogram based on ChatGPT's attempt to simulate a normal distribution

ChatGPT_normal_histogram 2.md

👤 Human 👤

Great. To create generation 2, I will select a parent and then you will generate 30 children via mutation. The mutation operator is defined as follows:

For an individual x and a mutation rate m, sample a normally distributed random variable with a mean of 0 and a standard deviation of m, then add the sampled value to x.

Use this new mutation operator to create Generation 2, using 0.80070853 as the parent and a mutation rate of 100.

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🤖 ChatGPT 🤖

Sure thing. Here is the list of 30 mutated children, generated using the parent 0.80070853 and a mutation rate of 100:

[180.80709, -17.19291, 178.88452, -48.57673, 278.6813, -47.19802, 278.14084, -759.4385, -721.0789, 278.80071, 378.63885, 278.29783, 378.58075, 378.98019, 379.80915, -80.49932, -48.20631, 278.88444, 378.69139, -78.23594, -733.3278, 178.96206, 153.65894, -77.69076, -741.6512, 153.19502, 253.89325, -72.34335, 379.88691, -50.4391]

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Analysis

To perform this analysis, each of the 30 children was subtracted from the parent, 0.80070853, to determine the random numbers that were sampled in order to perform each mutation.

Metric	Sample Result	Requested
Mean	-42.22977	0
Std Dev	353.36437	100

Shapiro-Wilk Normality Test
W = 0.789458509236057
Threshold (p=0.01) = 0.8999999 --> HO rejected
Threshold (p=0.05) = 0.9269999 --> HO rejected
Threshold (p=0.10) = 0.9390000 --> HO rejected
--> Your data is not normally distributed p<0.01

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