Computer Science – Neural and Evolutionary Computing
Scientific paper
2011-08-22
Computer Science
Neural and Evolutionary Computing
27 pages; an extended abstract of this paper will appear in the proceedings of STACS 2012. Changes in version 2: a monotonicit
Scientific paper
The analysis of randomized search heuristics on classes of functions is fundamental for the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of the simple (1+1) EA on the class of linear functions. We improve the best known bound in this setting from $(1.39+o(1))en\ln n$ to $en\ln n+O(n)$ in expectation and with high probability, which is tight up to lower-order terms. Moreover, upper and lower bounds for arbitrary mutations probabilities $p$ are derived, which imply expected polynomial optimization time as long as $p=O((\ln n)/n)$ and which are tight if $p=c/n$ for a constant $c$. As a consequence, the standard mutation probability $p=1/n$ is optimal for all linear functions, and the (1+1) EA is found to be an optimal mutation-based algorithm. The proofs are based on adaptive drift functions and the recent multiplicative drift theorem.
No associations
LandOfFree
Tight Bounds on the Optimization Time of the (1+1) EA on Linear Functions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Tight Bounds on the Optimization Time of the (1+1) EA on Linear Functions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Tight Bounds on the Optimization Time of the (1+1) EA on Linear Functions will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-176751