2022/04/01 by Marc Kaufmann, Maxime Larcher, Kaufmann, Marc +5
Decision Sciences · #68W50 #Auction Theory and Applications #FOS: Computer and information sciences #FOS: Mathematics #Neural and Evolutionary Computing (cs.NE) #Probability (math.PR)
paper · pdf · doi:10.48550/arxiv.2204.00531
openalex publication_date 2022/04/01 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We study the (1,λ)-EA with mutation rate c/n for c≤ 1, where the population size is adaptively controlled with the (1:s+1)-success rule. Recently, Hevia Fajardo and Sudholt have shown that this setup with c=1 is efficient on \onemax for s<1, but inefficient if s ≥ 18. Surprisingly, the hardest part is not close to the optimum, but rather at linear distance. We show that this behavior is not specific to \onemax. If s is small, then the algorithm is efficient on all monotone functions, and if s is large, then it needs superpolynomial time on all monotone functions. In the former case, for c<1 we show a O(n) upper bound for the number of generations and O(nlog n) for the number of function evaluations, and for c=1 we show O(nlog n) generations and O(n2loglog n) evaluations. We also show formally that optimization is always fast, regardless of s, if the algorithm starts in proximity of the optimum. All results also hold in a dynamic environment where the fitness function changes in each generation.