import numpy as np import ioh # ------------------------ (1+1)EA ------------------------ class OnePlusOneEA: budget_factor: int = 5 # evaluations = budget_factor * n seed = 42 def reset(self): # called between runs by ioh.Experiment; give each run a fresh ID self.algorithm_id = np.random.default_rng().integers(10**9) self.iterations = 0 def __call__(self, f, seed = 1): """ Run the algorithm on one IOH problem object (PBO). `f` is an IOH function object (callable). """ self.seed = seed rng = np.random.default_rng(self.seed) n = f.meta_data.n_variables budget = self.budget_factor * n * n self.mutation_rate = (1.0 / n) x = rng.integers(0,2,n) fx = f(x) while f.state.evaluations < budget: # sample ell ~ Bin(n, p); optionally condition on ell>0 ell = rng.binomial(n, self.mutation_rate) while ell < 1: ell = rng.binomial(n, self.mutation_rate) idx = rng.choice(n, size=ell, replace=False) y = x.copy() y[idx] = 1 - y[idx] # flip bits for 0/1 domain fy = f(y) if fy >= fx: x, fx = y, fy if fy >= f.optimum.y: break return fx, x