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Multiplicative Drift Analysis
This work introduces multiplicative drift analysis as a suitable way to analyze the runtime of randomized search heuristics such as evolutionary algorithms and demonstrates how it immediately gives natural proofs for the best known runtime bounds for the (1+1) Evolutionary Algorithm on combinatorial problems like finding minimum spanning trees, shortest paths, or Euler tours in graphs. Expand
From black-box complexity to designing new genetic algorithms
This work designs a new crossover-based genetic algorithm that uses mutation with a higher-than-usual mutation probability to increase the exploration speed and crossover with the parent to repair losses incurred by the more aggressive mutation. Expand
Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
It is proved that if its population size is chosen according to the one-fifth success rule then the expected optimization time on OneMax is linear, better than what any static population size λ can achieve and is asymptotically optimal also among all adaptive parameter choices. Expand
Multiplicative drift analysis
A multiplicative version of the classical drift theorem is proved, which gives natural proofs for the best known run-time bounds for the (1+1) Evolutionary Algorithm computing minimum spanning trees and shortest paths. Expand
Drift analysis and linear functions revisited
The classical problem how the (1+1) Evolutionary Algorithm optimizes an arbitrary linear pseudo-Boolean function is regarded, and it is shown that any such function is optimized in time (1 + o(1) 1.39en ln (n), which shows that for linear functions, even though the optimization behavior might differ, the resulting runtimes are very similar. Expand
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
IOHprofiler is a new tool for analyzing and comparing iterative optimization heuristics that provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and theFixed-budget function values. Expand
Unknown solution length problems with no asymptotically optimal run time
This work proves the first, almost matching, lower bounds for this setting, and shows that, for LeadingOnes, the (1 + 1) EA with any mutation operator treating zeros and ones equally has an expected run time of ω(n2 log(n) log log( n) ... log(s)(n)) when facing problem size n. Expand
Calculation of Discrepancy Measures and Applications
In this book chapter we survey known approaches and algorithms to compute discrepancy measures of point sets. After providing an introduction which puts the calculation of discrepancy measures in aExpand
Solving Problems with Unknown Solution Length at (Almost) No Extra Cost
This work analyzes variants of the (1+1) evolutionary algorithm for problems with unknown solution length and provides mutation rates suitable for settings in which neither the solution length nor the positions of the relevant bits are known. Expand
Optimal Static and Self-Adjusting Parameter Choices for the $$(1+(\lambda ,\lambda ))$$(1+(λ,λ)) Genetic Algorithm
It is proved that the self-adjusting parameter choice suggested in Doerr et al. (2015) outperforms all static choices and yields the conjectured linear expected runtime, which is asymptotically optimal among all possible parameter choices. Expand