Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Rigorous analyses of fitness-proportional selection for optimizing linear functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Blend of Markov-Chain and Drift Analysis
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
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Drift analysis, introduced to the field of evolutionary computation by He and Yao ten years ago, quickly became one of the strongest tools to prove upper and lower bounds on the run-times of evolutionary algorithms. It has, however, the reputation of being difficult to use, both because it relies on deeper mathematical tools and because it needs a clever guess of a potential function. In this tutorial, after presenting the classical results, I will focus on the recently developed multiplicative drift analysis method. It often is easier to employ and yields stronger results, e.g., run-time bounds that hold with high probability. I will end with a number of open problems of different difficulties. The intended audience of the tutorial has some basic experience in theory, though no particular prerequisites are required.