Repository navigation
Expand file tree
/
Copy pathOnePlusLambdaVarEA.py
More file actions
72 lines (56 loc) · 1.95 KB
/
Copy pathOnePlusLambdaVarEA.py
File metadata and controls
72 lines (56 loc) · 1.95 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
import numpy as np
import ioh
import math
# ------------------------ (1+1)EA ------------------------
class OnePlusLambdaVarEA:
lam: int = 10
budget_factor = 5
seed = 42
r : float = 2.0
F : float = 0.98
def __call__(self, f, seed) -> tuple[float, np.ndarray]:
"""
Run the (1+λ)EA on a single IOH problem object `f`.
Returns
-------
(fx_best, x_best) : tuple[float, np.ndarray]
Best fitness found and corresponding solution.
"""
self.seed = seed
rng = np.random.default_rng(self.seed)
n = f.meta_data.n_variables
budget = self.budget_factor * n * n
# Initialize x within bounds (PBO: binary {0,1})
x = rng.integers(0,2,n)
fx = f(x)
c = 0
while f.state.evaluations < budget:
# Generate λ offspring
best_y = None
best_fy = None
mean = self.r
std = math.sqrt(pow(self.F,c) * self.r * (1-self.r/n))
for _ in range(self.lam):
ell = min(math.floor(rng.normal(mean,std)),n / 2)
while ell < 1:
ell = min(math.floor(rng.normal(mean,std)),n / 2)
idx = rng.choice(n, size=ell, replace=False)
y = x.copy()
# bit flip for {0,1}: new = lb + ub - old (works for 0/1 bounds)
y[idx] = 1 - y[idx]
fy = f(y)
if best_y is None:
best_y, best_fy, win_r = y, fy, ell
else:
if fy > best_fy:
best_y, best_fy, win_r = y, fy, ell
if best_fy >= fx:
x, fx = best_y, best_fy
if best_fy >= f.optimum.y:
break
if win_r == self.r:
c += 1
else:
self.r = win_r
c = 0
return fx, x