The DeJong (De Jong) Test Suit F2
Hallo!! :D
Here another function from the The DeJong Test Suit.
The function is this:
F(x1,x2) = 100*(x1^2 - x2)^2 + (1 - x1)^2
The function is very simple and I got good values. The problem is to minimize the function. The global minimum is f(x1,x2) = 0 at (x1,x2) = (1,1).
ES Configuration:
Population = 60
Offspring = 60 (I used the (mu+mu)ES )
Generation = 5000
Simulation Time = 9.493 s
Below we have the function's graph:
Another view:
Now you see the ES performance. The horizontal axis is the number of generations ant the vertical axis is the best fitness.
A closer image from the image above:
The initial at first generation's values that I got were:
F(x1,x2) = 1.2143242479659295
x1 = -0.02275919812830051
x2 = 0.041540885751586681
The final values were:
F(x1,x2) = 1.6182695892245374e-008
x1 = 1.000118812318453
x2 = 1.0002330930642427
A good aproximation. :D
See You!!
Nosophorus
P.S: Next post I will show the Colville Function. This one has four variables. :D
Here another function from the The DeJong Test Suit.
The function is this:
F(x1,x2) = 100*(x1^2 - x2)^2 + (1 - x1)^2
The function is very simple and I got good values. The problem is to minimize the function. The global minimum is f(x1,x2) = 0 at (x1,x2) = (1,1).
ES Configuration:
Population = 60
Offspring = 60 (I used the (mu+mu)ES )
Generation = 5000
Simulation Time = 9.493 s
Below we have the function's graph:
Another view:
Now you see the ES performance. The horizontal axis is the number of generations ant the vertical axis is the best fitness.
A closer image from the image above:
The initial at first generation's values that I got were:
F(x1,x2) = 1.2143242479659295
x1 = -0.02275919812830051
x2 = 0.041540885751586681
The final values were:
F(x1,x2) = 1.6182695892245374e-008
x1 = 1.000118812318453
x2 = 1.0002330930642427
A good aproximation. :D
See You!!
Nosophorus
P.S: Next post I will show the Colville Function. This one has four variables. :D
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