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5 papers in cs.NE
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AgenticEvolve: Evolutionary Program Search with Persistent Language Model Agents that Design and Run Experiments
cs.NEcs.AIcs.LG Ze-Yu Peng, Qing-Yu Lan, Zhan-He Wang et al. · Oct 22, 2026

AgenticEvolve replaces the one-shot LLM variation operator in evolutionary program search with a persistent agent that edits code, runs self-designed experiments, and commits candidates for formal evaluation. By letting the agent test hypotheses locally between expensive evaluations, the framework aims to improve sample efficiency and final program quality on tasks ranging from online bin packing to symbolic regression. The authors report gains over a matched No-analysis ablation and over OpenEvolve, while noting that cost trade-offs depend heavily on model pricing.

Evolutionary program search with large language models keeps the structure of an evolutionary algorithm and uses the model as its variation operator. Typically, the model generates each candidate program in a single request from parent programs and their scores. We present AgenticEvolve, an evolutionary program search framework in which each island is run by a persistent large language model agent. Between evaluations, the agent designs and runs experiments on the candidate program. To measure the contribution of experimentation, we compare AgenticEvolve with No-analysis, an otherwise identical configuration with experiments disabled. On two heuristic design tasks with three large language models, AgenticEvolve produces better programs than No-analysis and OpenEvolve on both hidden and unseen instances. It reaches the baselines' final quality with far fewer evaluations and comparable cost. On twelve equation discovery problems, it recovers the ground-truth structure of more equations than No-analysis and existing language-model and genetic programming baselines.
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LESS: Lightweight Evolutionary Supernet Search in Minutes
cs.NEcs.LG Aviral Gandhi, Jinglue Xu, Jialong Li et al. · Oct 1, 2026

LESS introduces a lightweight neural architecture search pipeline that marries a brief fair supernet warm-up with candidate-conditioned hard-path updates inside a CMA-ES loop. The core claim is that just six balanced SGD steps per proposal are enough to sharply reduce selection regret, enabling NAS-Bench-201 searches in roughly seven minutes on a single RTX 4090 without retuning across datasets. If this speed–accuracy trade-off holds more broadly, it could lower the barrier to reproducible NAS experiments.

Low-cost NAS must both explore high-performing architectures and identify them reliably, yet reducing evaluation cost often weakens the fidelity of candidate comparisons. Training-free methods reduce evaluation cost by replacing learned task feedback with proxy signals measured at initialization. We introduce LESS (Lightweight Evolutionary Supernet Search), a data-driven method that combines a brief fair hard-path warm-up with discrete search under a single CMA-ES distribution. Each proposal is evaluated as its decoded hard genotype after six candidate-conditioned supernet updates. On NAS-Bench-201, LESS achieves \(93.189\pm0.467\%\) CIFAR-10 test accuracy in 409.1 seconds, coming within 0.04 percentage points of FairNAS using approximately \(1/24\) of its source-reported search time. Matched controls show that calibration improves selected validation accuracy by \(0.577\) percentage points while changing best-visited accuracy by only \(0.054\) points, indicating that its primary effect is to reduce selection regret. The frozen configuration transfers without tuning to CIFAR-100 and ImageNet16-120 with \(69.615\pm1.139\%\) and \(43.720\pm1.697\%\) accuracy. Applied without tuning to the larger DARTS space, LESS achieves \(96.95\pm0.14\%\) on CIFAR-10 and \(82.43\pm0.80\%\) on CIFAR-100, with each search completing in approximately 43.5 minutes on a single GPU. Together, these results show that short, balanced, data-dependent updates enable competitive neural architecture search across datasets and search spaces within minutes.
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arXivPDF
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TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design
cs.NEcs.LG Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez · Oct 1, 2026

TRACE targets dynamic resource assignment in industrial systems where processing times, energy profiles, and queue capacities are unknown a priori. While existing LLM-based Automatic Heuristic Design learns only from scalar fitness scores, TRACE couples an evolutionary loop with an agentic workflow that distills noisy system logs into insights and executable surrogate models. This feedback turns blind trial-and-error into data-driven design, yielding heuristics that cut cost by over an order of magnitude relative to prior AHD methods and reduce energy by 65% against industry standards without sacrificing reliability.

Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in research, industrial deployments still rely on hand-written rules that operators can read, audit, and execute within tight latency budgets. LLM-based Automatic Heuristic Design (AHD) promises to automate writing such rules. However, existing AHD frameworks were developed for combinatorial problems fully specified to the LLM, and they learn only from a scalar fitness score. In real systems, the behaviour that determines a good heuristic, such as processor speeds or power consumption, is unknown a priori: the score reveals which heuristic performs better, but not why. This missing information is recorded in the system logs that every evaluation produces. Exploiting it is non-trivial: logs are massive and noisy, the relevant signals depend on the objective, and their content and format vary across hardware and software stacks, so they can neither be fed to an LLM as is nor processed by a fixed parser. We propose TRACE, which couples an evolutionary AHD loop with an agentic knowledge-extraction workflow. A Reasoner agent analyzes the log schema in light of the objective and formulates hypotheses about the system dynamics; a Coder agent writes and executes schema-specific code to test them, producing insights or executable tools for the evolved heuristics. We evaluate TRACE on a synthetic cloud benchmark and a 5G vRAN scenario built from industrial testbed measurements and operational traffic traces. TRACE consistently outperforms state-of-the-art AHD methods in resource assignment problems and yields more auditable heuristics at under 2% overhead.
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Continual Reinforcement Learning with Neuroevolution
cs.NEcs.LG Eleni Nisioti, Andrea Cossu, Kathrin Korte et al. · Oct 1, 2026

Continual reinforcement learning demands a delicate balance between plasticity (learning new tasks) and stability (not forgetting old ones). This paper argues that neuroevolution—specifically evolution strategies (ES) and genetic algorithms (GAs)—offers a compelling alternative to gradient-based policy-gradient methods, which tend to lose plasticity as tasks change. Across a broad sweep of environments and task-shift types, the authors show that ES consistently achieves the best stability-plasticity trade-off, and they propose a clear mechanism: parameter-space exploration produces wider "neighborhoods" of robust solutions that overlap across sequential tasks. The work matters because it challenges the prevailing focus on fixing gradient-based RL and instead revives a classic optimization paradigm with strong empirical and conceptual justification.

Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet consistently achieved a good balance between adaptation and forgetting. Here we turn to an alternative optimization paradigm, neuroevolution (NE): algorithms that search directly in weight space through mutation and selection over a population of neural networks. Across a wide array of environments and environmental changes, with policies ranging from a few hundred parameters to million-parameter networks, we compare evolution strategies (ES) and genetic algorithms (GAs) against state-of-the-art continual RL variants and population-based RL. ES most consistently achieves a good stability-plasticity trade-off, while the GA is the most plastic method but forgets more than ES. To explain this, we study the return landscape around each method's solutions. ES finds the widest neighborhoods, i.e.\ regions of weight space in which perturbed policies still solve the task, and the size of the overlap between the neighborhoods of consecutive tasks correlates with a method's stability-plasticity trade-off. Rewarding behavioral diversity in a GA through novelty search makes the population even more plastic, at the cost of forgetting. Finally, symptoms of plasticity loss commonly reported in RL do not transfer to NE. Overall, these results establish NE as a competitive alternative to RL under continual task changes, and suggest that training under perturbations in weight space may be a useful mechanism for continual learning more broadly.
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arXivPDF
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Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons
cs.NEcs.AI Kesheng Chen, Wenjian Luo, Xin Lin et al. · Mar 23, 2026

Unmanned aerial vehicle (UAV) path planning traditionally treats efficiency and safety objectives as a single multiobjective optimization problem. This paper proposes a biparty multiobjective formulation with separate decision-makers for efficiency and safety, adapting immune algorithms (NNIA, HEIA, AIMA) into BPNNIA, BPHEIA, and BPAIMA to find common Pareto optimal solutions. The work addresses the practical scenario where regulatory and operational departments have independent criteria.

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.
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arXivPDF