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Evolutionary Algorithm
Representation, Mutation, Crossover (Binary…
Evolutionary Algorithm
Representation, Mutation, Crossover
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Integer
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Mutation
Random Reset Mutation
- New value is chosen for each gene with probability, p
"Creep" Mutation
- Add a small value(+/-) to each gene with probability, p
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Permutation
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Mutation
- Normal mutation may lead
inadmissible solutions
- Must change at least 2 genes
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Scramble Mutation
- Take subset of genes & random rearrange
Inversion Mutation
- Take subset of genes & invert position
Crossover
- Normal crossover may lead
inadmissible solutions
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Tree Structure
Mutation
2 Parameter:
- Probability to choose mutation
- Probability to choose an point as root of subtree
to be replace
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Crossover
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2 Parameter:
- Probability to choose recombination/crossover
- Probability to choose an point within each parent