Abstract
We study sorting of permutations by random swaps if each comparison gives the wrong result with some fixed probability $$p<1/2$$ . We use this process as prototype for the behaviour of randomized, comparison-based optimization heuristics in the presence of noisy comparisons. As quality measure, we compute the expected fitness of the stationary distribution. To measure the runtime, we compute the minimal number of steps after which the average fitness approximates the expected fitness of the stationary distribution. We study the process where in each round a random pair of elements at distance at most r are compared. We give theoretical results for the extreme cases $$r=1$$ and $$r=n$$ , and experimental results for the intermediate cases. We find a trade-off between faster convergence (for large r) and better quality of the solution after convergence (for small r).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
