The cGA optimizes jump functions with jump size up to (1/20)ln n in O(n log n) generations when the population parameter is chosen well, and needs exp(Ω(k)) generations for large jumps no matter the parameter.
The (1 + ( , )) GA is even faster on multimodal problems
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The Runtime of the Compact Genetic Algorithm on Jump Functions
The cGA optimizes jump functions with jump size up to (1/20)ln n in O(n log n) generations when the population parameter is chosen well, and needs exp(Ω(k)) generations for large jumps no matter the parameter.