Can you explain the role of mutation in genetic algorithms?
Sure! Mutation introduces random changes to individuals in a population, helping to maintain genetic diversity.
Why is genetic diversity important in these algorithms?
Genetic diversity helps prevent premature convergence to suboptimal solutions, allowing the algorithm to explore a wider solution space.
How does mutation differ from crossover in genetic algorithms?
Mutation randomly alters genes, while crossover combines genes from two parents to create offspring, promoting new solutions.
Are there different types of mutation techniques used?
Yes, common techniques include bit-flip mutation, swap mutation, and Gaussian mutation, each suited for different problem types.
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