What are the key parameters in a genetic algorithm?
The key parameters in a genetic algorithm include population size, mutation rate, crossover rate, and selection method.
How does the mutation rate affect the algorithm's performance?
A higher mutation rate can introduce more diversity, helping to avoid local optima, but too high can disrupt convergence.
What is the ideal population size for a genetic algorithm?
The ideal population size varies, but it should be large enough to maintain genetic diversity while being computationally feasible.
Can you explain the crossover rate and its significance?
The crossover rate determines how often two parent solutions combine to create offspring, influencing exploration and exploitation.
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