## Crynodeb

The (1 + (λ, λ)) genetic algorithm, first proposed at GECCO 2013, showed a surprisingly good performance on some optimization problems. The theoretical analysis so far was restricted to the OneMax test function, where this GA profited from the perfect fitness-distance correlation. In this work, we conduct a rigorous runtime analysis of this GA on random 3-SAT instances in the planted solution model having at least logarithmic average degree, which are known to have a weaker fitness distance correlation.

We prove that this GA with fixed not too large population size again obtains runtimes better than Θ(n log n), which is a lower bound for most evolutionary algorithms on pseudo-Boolean problems with unique optimum. However, the self-adjusting version of the GA risks reaching population sizes at which the intermediate selection of the GA, due to the weaker fitness-distance correlation, is not able to distinguish a profitable offspring from others. We show that this problem can be overcome by equipping the self-adjusting GA with an upper limit for the population size. Apart from sparse instances, this limit can be chosen in a way that the asymptotic performance does not worsen compared to the idealistic OneMax case. Overall, this work shows that the (1 + (λ, λ)) GA can provably have a good performance on combinatorial search and optimization problems also in the presence of a weaker fitness-distance correlation.

We prove that this GA with fixed not too large population size again obtains runtimes better than Θ(n log n), which is a lower bound for most evolutionary algorithms on pseudo-Boolean problems with unique optimum. However, the self-adjusting version of the GA risks reaching population sizes at which the intermediate selection of the GA, due to the weaker fitness-distance correlation, is not able to distinguish a profitable offspring from others. We show that this problem can be overcome by equipping the self-adjusting GA with an upper limit for the population size. Apart from sparse instances, this limit can be chosen in a way that the asymptotic performance does not worsen compared to the idealistic OneMax case. Overall, this work shows that the (1 + (λ, λ)) GA can provably have a good performance on combinatorial search and optimization problems also in the presence of a weaker fitness-distance correlation.

Iaith wreiddiol | Saesneg |
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Teitl | GECCO '17 |

Is-deitl | Proceedings of the Genetic and Evolutionary Computation Conference |

Cyhoeddwr | Association for Computing Machinery |

Tudalennau | 1343-1350 |

Nifer y tudalennau | 8 |

ISBN (Argraffiad) | 978-1-4503-4920-8 |

Dynodwyr Gwrthrych Digidol (DOIs) | |

Statws | Cyhoeddwyd - 01 Gorff 2017 |

Cyhoeddwyd yn allanol | Ie |

Digwyddiad | GECCO 2017: The Genetic and Evolutionary Computation Conference - Hyd: 15 Gorff 2017 → 19 Gorff 2017 |

### Cynhadledd

Cynhadledd | GECCO 2017 |
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Cyfnod | 15 Gorff 2017 → 19 Gorff 2017 |