# Drift Analysis and Evolutionary Algorithms Revisited

arXiv: https://arxiv.org/abs/1608.03226 · HTML: https://mlsanity.com/paper/1608.03226/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [Choosing the Right Algorithm With Hints From Complexity Theory](https://mlsanity.com/paper/2109.06584/) (2021)
- [Many Objective Problems Where Crossover is Provably Essential](https://mlsanity.com/paper/2412.18375/) (2024)
- [Plus Strategies are Exponentially Slower for Planted Optima of Random Height](https://mlsanity.com/paper/2404.09687/) (2024)

## Abstract (arXiv)

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a boolean function f:0,1n → mathbb R. The algorithm starts with a random search point xi ∈ 0,1n, 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') ≥ 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 review known results and provide new and self-contained proofs of partly stronger results.