Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.
|Original language||English (US)|
|Title of host publication||GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|State||Published - Jul 20 2016|
|Event||2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States|
Duration: Jul 20 2016 → Jul 24 2016
|Name||GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference|
|Other||2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion|
|Period||7/20/16 → 7/24/16|
Bibliographical notePublisher Copyright:
© 2016 Copyright held by the owner/author(s).
Copyright 2017 Elsevier B.V., All rights reserved.
- Ant Colony Optimization
- Estimation of Distribution Algorithm
- Evolutionary Algorithm
- Genetic Algorithm