crossingIndividual_v1 | R Documentation |
This function generates a new individual by combining genes from two parent individuals. It samples a subset of genes from the parents' combined gene pool, with the child’s gene sparsity determined based on the parents' sparsity levels.
crossingIndividual_v1(X, y, clf, parent1, parent2)
X |
A matrix or data frame of feature values, where each row represents a feature and each column represents a sample. |
y |
A response vector or target variable for supervised learning. The
length of |
clf |
A classifier object containing parameters for generating a new individual. |
parent1 |
A list representing the first parent, which includes 'indices_' (genes) and 'eval.sparsity' (gene sparsity). |
parent2 |
A list representing the second parent, also with 'indices_' and 'eval.sparsity'. |
The function combines the genes of 'parent1' and 'parent2' into a unique pool and selects a subset for the child. The child’s sparsity level is randomly determined based on the range between the two parents' 'eval.sparsity'. If the parents have identical sparsity, a subset is sampled within the parent's sparsity limit.
**Note**: The function currently has limitations and may need further development to handle edge cases and differing gene pools effectively.
A list representing the new individual, with a subset of genes ('indices') inherited from the parents.
## Not run:
X <- matrix(rnorm(100), nrow = 10) # Random features
y <- sample(c(0, 1), 10, replace = TRUE) # Random binary response
clf <- list() # Placeholder for classifier settings
parent1 <- list(indices_ = c(1, 3, 5, 7), eval.sparsity = 4)
parent2 <- list(indices_ = c(2, 4, 6, 8), eval.sparsity = 4)
child <- crossingIndividual_v1(X, y, clf, parent1, parent2)
print(child)
## End(Not run)
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