{"paper":{"title":"Drift Analysis and Evolutionary Algorithms Revisited","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","math.PR"],"primary_cat":"math.CO","authors_text":"Angelika Steger, Johannes Lengler","submitted_at":"2016-08-10T16:14:57Z","abstract_excerpt":"One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a boolean function $f:\\{0,1\\}^n \\to {\\mathbb R}$. The algorithm starts with a random search point $\\xi \\in \\{0,1\\}^n$, and in each round it flips each bit of $\\xi$ with probability $c/n$ independently at random, where $c>0$ is a fixed constant. The thus created offspring $\\xi'$ replaces $\\xi$ if and only if $f(\\xi') \\ge f(\\xi)$. The analysis of the runtime of this simple algorithm on monotone and on linear functions turned out to be highly non-trivial. In this paper we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.03226","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}