### Abstract

Recently Ant Colony Optimization (ACO) algorithms have been proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses focus on combinatorial problems, such as path finding. We analyze an ACO algorithm in a setting where we try to optimize the simple OneMax test function, but with additive posterior noise sampled from a Gaussian distribution. Without noise the classical (μ + 1)-EA outperforms any ACO algorithm, with smaller μ being better; however, with large noise, the (μ + 1)-EA fails, even for high values of μ (which are known to help against small noise). In this paper we show that ACO is able to deal with arbitrarily large noise in a graceful manner, that is, as long as the evaporation factor ρ is small enough dependent on the parameter σ^{2} of the noise and the dimension n of the search space (ρ = o(1/(n(n+ σ log n)^{2} logn))), optimization will be successful.

Original language | English (US) |
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Title of host publication | GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference |

Editors | Sara Silva |

Publisher | Association for Computing Machinery, Inc |

Pages | 17-24 |

Number of pages | 8 |

ISBN (Electronic) | 9781450334723 |

DOIs | |

State | Published - Jul 11 2015 |

Event | 16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: Jul 11 2015 → Jul 15 2015 |

### Publication series

Name | GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference |
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### Other

Other | 16th Genetic and Evolutionary Computation Conference, GECCO 2015 |
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Country | Spain |

City | Madrid |

Period | 7/11/15 → 7/15/15 |

### Keywords

- Ant colony optimization
- Noisy fitness
- Run time analysis
- Theory

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## Cite this

*GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference*(pp. 17-24). (GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2739480.2754723