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Found 6 papers, 4 papers with code
Faster Optimization Through Genetic Drift
For the classical benchmark OneMax, the cGA has to two different modes of operation: a conservative one with small step sizes $\Theta(1/(\sqrt{n}\log n))$, which is slow but prevents genetic drift, and an aggressive one with large step sizes $\Theta(1/\log n)$, in which genetic drift leads to wrong decisions, but those are corrected efficiently.
Hardest Monotone Functions for Evolutionary Algorithms
Recently, Kaufmann, Larcher, Lengler and Zou conjectured that for the self-adjusting $(1,\lambda)$-EA, Adversarial Dynamic BinVal (ADBV) is the hardest dynamic monotone function to optimize.
OneMax is not the Easiest Function for Fitness Improvements
In this paper we disprove this conjecture and show that OneMax is not the easiest fitness landscape with respect to finding improving steps.
Self-adjusting Population Sizes for the $(1, λ)$-EA on Monotone Functions
Recently, Hevia Fajardo and Sudholt have shown that this setup with $c=1$ is efficient on \onemax for $s<1$, but inefficient if $s \ge 18$.
Projection Bias in Effort Choices
Finally, when they have either increasing or decreasing productivity, people work less each day than previously planned.
Narrow Bracketing in Work Choices
Many important economic outcomes result from cumulative effects of smaller choices, so the best outcomes require accounting for other choices at each decision point.
