Q-BIOLAT is a framework that converts pretrained protein-language-model embeddings into compact binary codes and trains a quadratic unconstrained binary optimization (QUBO) surrogate with unary and pairwise latent interactions for protein fitness optimization. The study demonstrates that binary encodings with similar predictive accuracy can produce different Hamming neighborhoods, affecting local optima and search trajectories, and shows that PCA followed by per‑coordinate median thresholding yields a more balanced binary space than AE/VAE baselines. Experimental evaluation on GFP and AAV fitness landscapes from ProteinGym confirms that simulated annealing, genetic algorithms, and greedy hill climbing can retrieve high‑percentile variants, with decoded candidates reported via surrogate‑predicted scores.
By Truong-Son Hy
arXiv:2608. 19487v1 Announce Type: cross Abstract: Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intelligence (AI), and machine learning.
By Heyang Thomas Li, Alexander Pletzer, Yuan Tian, Yi Mei, Mengjie Zhang
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
By Hui Xue, Fan Yang
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.
By Lu Han, Jin Wang, Yuchen Li, Haoran Gu, Shulei Liu, Ziyang Shi, Wenao Lu, Handing Wang
arXiv:2609.13247v1 Announce Type: cross
Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costl...
By Duguma Yeshitla Habtemariam, Jihwan Lee
arXiv:2609.13533v1 Announce Type: cross
Abstract: Most hyperparameter configurations for Evolvable-Substrate HyperNEAT (ES-HyperNEAT) produce networks that stagnate at random-guessing performance, wa...
By Romain Claret, Arthur Gygax, Michael O'Neill, Paul Cotofrei, Pascal Felber
arXiv:2609.05992v1 Announce Type: new
Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task complet...
By Hengle Jiang, Qijun Cai, Ziying Luo, Ke Tang
arXiv:2608. 05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.
By Sichun Luo, Yi Huang, Guanzhi Deng, Haibo Wang, Haochen Luo, Lei Li, Zefa Hu, Junlan Feng, Qi Liu
arXiv:2609.17067v1 Announce Type: cross
Abstract: Indirectly encoded neural networks can assign different activation functions to individual nodes, but the right functions are rarely known in advance...
By Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel
arXiv:2608. 07645v1 Announce Type: new Abstract: Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks.
By Changzhi Liu, Yilun Liu, Sikuan Yan, Volker Tresp, Yunpu Ma
The paper introduces TGL-NSGA-II, a low‑fidelity framework that uses a pretrained teacher to stratify samples by difficulty and class, then applies a short knowledge‑distillation step (KD‑Lite) before scoring candidates on a stratified evaluation set. The teacher‑guided scores are fused with a Gaussian‑process surrogate to select candidates for full evaluation, and the method is evaluated on keyword spotting and bird‑call classification tasks. Results show high Kendall‑τ values (0.74 and 0.62), a 41% reduction in proxy‑score variance, and improved hypervolume and false‑positive rates compared to full NSGA‑II, while running 2.2× faster under a constrained evaluation budget.
By Soumen Garai, Suman Samui