Runtime Analysis of Fitness-Proportionate Selection on Linear Functions
Duc-Cuong Dang,
Anton Eremeev,
Per Kristian Lehre
Abstract:This paper extends the runtime analysis of non-elitist evolutionary algorithms (EAs) with fitness-proportionate selection from the simple OneMax function to the linear functions. Not only does our analysis cover a larger class of fitness functions, it also holds for a wider range of mutation rates. We show that with overwhelmingly high probability, no linear function can be optimised in less than exponential time, assuming bitwise mutation rate Θ(1/n) and population size λ = n k for any constant k > 2. In cont… Show more
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