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@@ 51-83 (lines=33) @@
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x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))] |
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return asarray(x) |
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def CrossBest1(pop, ic, x_b, f, cr, rnd=rand, *args): |
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r"""Mutation strategy with crossover. |
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Mutation strategy uses two different random individuals from population and global best individual. |
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Mutation: |
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Name: de/best/1 |
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:math:`\mathbf{v}_{i, G} = \mathbf{x}_{best, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G})` |
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where :math:`r_1, r_2` are random indexes representing current population individuals. |
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Crossover: |
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Name: Binomial crossover |
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:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}` |
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args: |
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pop (numpy.ndarray[Individual]): Current population. |
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ic (int): Index of individual being mutated. |
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x_b (Individual): Current global best individual. |
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f (float): Scale factor. |
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cr (float): Crossover probability. |
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rnd (mtrand.RandomState): Random generator. |
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*args (list): Additional arguments. |
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returns: |
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numpy.ndarray: Mutated and mixed individual. |
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""" |
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j = rnd.randint(len(pop[ic])) |
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p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 2 else None |
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r = rnd.choice(len(pop), 2, replace=not len(pop) >= 2, p=p) |
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x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))] |
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return asarray(x) |
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def CrossRand2(pop, ic, x_b, f, cr, rnd=rand, *args): |
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r"""Mutation strategy with crossover. |
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@@ 17-49 (lines=33) @@
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logger = logging.getLogger('NiaPy.algorithms.basic') |
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logger.setLevel('INFO') |
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def CrossRand1(pop, ic, x_b, f, cr, rnd=rand, *args): |
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r"""Mutation strategy with crossover. |
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Mutation strategy uses three different random individuals from population to perform mutation. |
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Mutation: |
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Name: DE/rand/1 |
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:math:`\mathbf{x}_{r_1, G} + F \cdot (\mathbf{x}_{r_2, G} - \mathbf{x}_{r_3, G}` |
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where :math:`r_1, r_2, r_3` are random indexes representing current population individuals. |
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Crossover: |
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Name: Binomial crossover |
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:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}` |
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Args: |
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pop (numpy.ndarray[Individual]): Current population. |
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ic (int): Index of individual being mutated. |
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x_b (Individual): Current global best individual. |
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f (float): Scale factor. |
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cr (float): Crossover probability. |
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rnd (mtrand.RandomState): Random generator. |
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*args (list): Additional arguments. |
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Returns: |
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numpy.ndarray: Mutated and mixed individual. |
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""" |
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j = rnd.randint(len(pop[ic])) |
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p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 3 else None |
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r = rnd.choice(len(pop), 3, replace=not len(pop) >= 3, p=p) |
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x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))] |
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return asarray(x) |
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def CrossBest1(pop, ic, x_b, f, cr, rnd=rand, *args): |
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r"""Mutation strategy with crossover. |
NiaOrg /
NiaPy
