output
20162026
most citedLanguage Model Crossover: Variation through Few-Shot Prompting

63 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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9 papers · 1 filter

cs.NE2026

Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models

Hormoz Shahrzad, Niharika Gajawelli, Kaitlin Maile +2

Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. St…

cs.NE2026

Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution

Rivaaj Monsia, Daniel Young, Olivier Francon +1

Ensuring the microbiological safety of large, heterogeneous water distribution systems (WDS) typically requires managing appropriate levels of disinfectant residuals including chlo…

cs.NE2024★ 3 cited

GPU-Accelerated Rule Evaluation and Evolution

Hormoz Shahrzad, Risto Miikkulainen

This paper introduces an innovative approach to boost the efficiency and scalability of Evolutionary Rule-based machine Learning (ERL), a key technique in explainable AI. While tra…

cs.NE2023

Discovering Effective Policies for Land-Use Planning with Neuroevolution

Daniel Young, Olivier Francon, Elliot Meyerson +5

How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate chang…

cs.NE2023★ 63 cited

Language Model Crossover: Variation through Few-Shot Prompting

Elliot Meyerson, Mark J. Nelson, Herbie Bradley +4

This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models o…

cs.NE2023

Accelerating Evolution Through Gene Masking and Distributed Search

Hormoz Shahrzad, Risto Miikkulainen

In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in o…

cs.NE2022★ 4 cited

Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)

Elliot Meyerson, Xin Qiu, Risto Miikkulainen

This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents…

cs.NE2020★ 2 cited

The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings

Elliot Meyerson, Risto Miikkulainen

This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is…

cs.NE2018★ 2 cited

A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing

Jingbo Jiang, Diego Legrand, Robert Severn +1

Multivariate testing has recently emerged as a promising technique in web interface design. In contrast to the standard A/B testing, multivariate approach aims at evaluating a larg…