"Genetic algorithms borrow from biological evolution, using populations of candidate solutions, mutation, crossover, selection, and fitness functions to search for better solutions."
Holland explains how genetic algorithms translate principles of biological evolution into computational search and optimization procedures. The fitness function determines which candidate solutions contribute to subsequent generations, making the formulation of the optimization objective central to the system's behaviour.
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