# Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise

arXiv: https://arxiv.org/abs/2305.04553 · HTML: https://mlsanity.com/paper/2305.04553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 18 of 18)

- [Fast Genetic Algorithms](https://mlsanity.com/paper/1703.03334/) (2017)
- [Does Comma Selection Help To Cope With Local Optima](https://mlsanity.com/paper/2004.01274/) (2020)
- [Self-Adjusting Evolutionary Algorithms for Multimodal Optimization](https://mlsanity.com/paper/2004.03266/) (2020)
- [Better Runtime Guarantees Via Stochastic Domination](https://mlsanity.com/paper/1801.04487/) (2018)
- [The Runtime of the Compact Genetic Algorithm on Jump Functions](https://mlsanity.com/paper/1908.06527/) (2019)
- [Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution](https://mlsanity.com/paper/2104.06714/) (2021)
- [Runtime Analysis of a Heavy-Tailed (1+(λ,λ)) Genetic Algorithm on Jump Functions](https://mlsanity.com/paper/2006.03523/) (2020)
- [A Tight Runtime Analysis for the (μ + λ) EA](https://mlsanity.com/paper/1812.11061/) (2018)
- [A Rigorous Runtime Analysis of the (1 + (λ, λ)) GA on Jump Functions](https://mlsanity.com/paper/2004.06702/) (2020)
- [Runtime Analysis of the (1+(λ,λ)) Genetic Algorithm on Random Satisfiable 3-CNF Formulas](https://mlsanity.com/paper/1704.04366/) (2017)
- [Significance-based Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/1807.03495/) (2018)
- [Level-Based Analysis of the Univariate Marginal Distribution Algorithm](https://mlsanity.com/paper/1807.10038/) (2018)
- [Fast Artificial Immune Systems](https://mlsanity.com/paper/1806.00299/) (2018)
- [Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial](https://mlsanity.com/paper/1812.00966/) (2018)
- [How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs](https://mlsanity.com/paper/2304.10414/) (2023)
- [Upper Bounds on the Runtime of the Univariate Marginal Distribution Algorithm on OneMax](https://mlsanity.com/paper/1704.00026/) (2017)
- [The (1+(λ,λ)) Global SEMO Algorithm](https://mlsanity.com/paper/2210.03618/) (2022)
- [Lasting Diversity and Superior Runtime Guarantees for the (μ+1) Genetic Algorithm](https://mlsanity.com/paper/2302.12570/) (2023)

## Abstract (arXiv)

Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analyze to what extent the (1+(λ,λ)) genetic algorithm is robust to noise. This algorithm also works with larger offspring population sizes, but an intermediate selection step and a non-standard use of crossover as repair mechanism could render this algorithm less robust than, e.g., the simple (1+λ) evolutionary algorithm. Our experimental analysis on several classic benchmark problems shows that this difficulty does not arise. Surprisingly, in many situations this algorithm is even more robust to noise than the (1+λ)~EA.