Search Results for author:
Found 5 papers, 3 papers with code
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.
Gated recurrent neural networks discover attention
In particular, we examine RNNs trained to solve simple in-context learning tasks on which Transformers are known to excel and find that gradient descent instills in our RNNs the same attention-based in-context learning algorithm used by Transformers.
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 $s \ge 18$.
Solving Static Permutation Mastermind using $O(n \log n)$ Queries
Permutation Mastermind is a version of the classical mastermind game in which the number of positions $n$ is equal to the number of colors $k$, and repetition of colors is not allowed, neither in the codeword nor in the queries.
Combinatorics Probability
