arXiv:2603. 18385v3 Announce Type: replace-cross Abstract: We present a new solution concept called evolutionarily stable Stackelberg equilibrium (SESS).
By Sam Ganzfried
The paper introduces an operator calculus for population-based optimization, treating each update rule—such as mutation, selection, or recombination—as an operator. By establishing explicit regularity and small‑step conditions, the authors show that the leading effects of these operators add, allowing convergence guarantees to be reused as modular building blocks. The framework differentiates between finding and retaining good solutions, reducing the population’s mean objective, and concentrating candidates near an optimizer, and it identifies additional approximation conditions required for finite‑budget guarantees. Applications to distribution adaptation, recombinative evolution, and consensus dynamics are verified, including nonconvex cases, and controlled experiments illustrate how component effects vary with population geometry and performance measures.
By Pekka Malo, Lauri Viitasaari, Patrik Nummi, Antti Suominen, Ankur Sinha, Olli Tahvonen
The paper introduces a new framework for genetic algorithms where mutation and recombination operators are guided by machine‑learning optimization rather than random changes. It shows that such operators can improve objective values but at higher computational cost, and demonstrates three key phenomena: the necessity of solution‑pool diversity for parity learning, the simultaneous need for generation, mutation, and recombination to achieve near‑optimal solutions, and a phase transition in Gaussian settings where positive drift yields exponential speedup.
By Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
arXiv:2606. 12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems.
By Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel
The paper draws a parallel between AI model development and population genetics, treating successive model generations as analogous to sexual and asexual reproduction. It demonstrates that training models on peers’ outputs reproduces classic genetic processes such as the Wright–Fisher model, while combining parents’ weights can either cancel or preserve inherited advantages depending on the method. Experiments across recurrent, feedforward, and language models confirm these analogies and reveal architecture‑specific biases, including the Fisher‑Muller effect and reproductive isolation when lineages learn conflicting conventions.
By Giorgio F. Gilestro
arXiv:2512. 10279v3 Announce Type: replace-cross Abstract: We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information.
By Sam Ganzfried