arXiv - CS - Neural and Evolutionary Computing最新文献_第3页

Runtime analysis of a coevolutionary algorithm on impartial combinatorial games 公正组合博弈协同进化算法的运行分析

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-06 DOI: arxiv-2409.04177

Alistair Benford, Per Kristian Lehre

{"title":"Runtime analysis of a coevolutionary algorithm on impartial combinatorial games","authors":"Alistair Benford, Per Kristian Lehre","doi":"arxiv-2409.04177","DOIUrl":"https://doi.org/arxiv-2409.04177","url":null,"abstract":"Due to their complex dynamics, combinatorial games are a key test case and\u0000application for algorithms that train game playing agents. Among those\u0000algorithms that train using self-play are coevolutionary algorithms (CoEAs).\u0000CoEAs evolve a population of individuals by iteratively selecting the strongest\u0000based on their interactions against contemporaries, and using those selected as\u0000parents for the following generation (via randomised mutation and crossover).\u0000However, the successful application of CoEAs for game playing is difficult due\u0000to pathological behaviours such as cycling, an issue especially critical for\u0000games with intransitive payoff landscapes. Insight into how to design CoEAs to avoid such behaviours can be provided by\u0000runtime analysis. In this paper, we push the scope of runtime analysis to\u0000combinatorial games, proving a general upper bound for the number of simulated\u0000games needed for UMDA (a type of CoEA) to discover (with high probability) an\u0000optimal strategy for an impartial combinatorial game. This result applies to\u0000any impartial combinatorial game, and for many games the implied bound is\u0000polynomial or quasipolynomial as a function of the number of game positions.\u0000After proving the main result, we provide several applications to simple\u0000well-known games: Nim, Chomp, Silver Dollar, and Turning Turtles. As the first\u0000runtime analysis for CoEAs on combinatorial games, this result is a critical\u0000step towards a comprehensive theoretical framework for coevolution.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models 通过大型语言模型推进进化多任务中的自动知识转移

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-06 DOI: arxiv-2409.04270

Yuxiao Huang, Xuebin Lv, Shenghao Wu, Jibin Wu, Liang Feng, Kay Chen Tan

{"title":"Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models","authors":"Yuxiao Huang, Xuebin Lv, Shenghao Wu, Jibin Wu, Liang Feng, Kay Chen Tan","doi":"arxiv-2409.04270","DOIUrl":"https://doi.org/arxiv-2409.04270","url":null,"abstract":"Evolutionary Multi-task Optimization (EMTO) is a paradigm that leverages\u0000knowledge transfer across simultaneously optimized tasks for enhanced search\u0000performance. To facilitate EMTO's performance, various knowledge transfer\u0000models have been developed for specific optimization tasks. However, designing\u0000these models often requires substantial expert knowledge. Recently, large\u0000language models (LLMs) have achieved remarkable success in autonomous\u0000programming, aiming to produce effective solvers for specific problems. In this\u0000work, a LLM-based optimization paradigm is introduced to establish an\u0000autonomous model factory for generating knowledge transfer models, ensuring\u0000effective and efficient knowledge transfer across various optimization tasks.\u0000To evaluate the performance of the proposed method, we conducted comprehensive\u0000empirical studies comparing the knowledge transfer model generated by the LLM\u0000with existing state-of-the-art knowledge transfer methods. The results\u0000demonstrate that the generated model is able to achieve superior or competitive\u0000performance against hand-crafted knowledge transfer models in terms of both\u0000efficiency and effectiveness.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pareto Set Prediction Assisted Bilevel Multi-objective Optimization 帕累托集合预测辅助双层多目标优化

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-05 DOI: arxiv-2409.03328

Bing Wang, Hemant K. Singh, Tapabrata Ray

{"title":"Pareto Set Prediction Assisted Bilevel Multi-objective Optimization","authors":"Bing Wang, Hemant K. Singh, Tapabrata Ray","doi":"arxiv-2409.03328","DOIUrl":"https://doi.org/arxiv-2409.03328","url":null,"abstract":"Bilevel optimization problems comprise an upper level optimization task that\u0000contains a lower level optimization task as a constraint. While there is a\u0000significant and growing literature devoted to solving bilevel problems with\u0000single objective at both levels using evolutionary computation, there is\u0000relatively scarce work done to address problems with multiple objectives\u0000(BLMOP) at both levels. For black-box BLMOPs, the existing evolutionary\u0000techniques typically utilize nested search, which in its native form consumes\u0000large number of function evaluations. In this work, we propose to reduce this\u0000expense by predicting the lower level Pareto set for a candidate upper level\u0000solution directly, instead of conducting an optimization from scratch. Such a\u0000prediction is significantly challenging for BLMOPs as it involves one-to-many\u0000mapping scenario. We resolve this bottleneck by supplementing the dataset using\u0000a helper variable and construct a neural network, which can then be trained to\u0000map the variables in a meaningful manner. Then, we embed this initialization\u0000within a bilevel optimization framework, termed Pareto set prediction assisted\u0000evolutionary bilevel multi-objective optimization (PSP-BLEMO). Systematic\u0000experiments with existing state-of-the-art methods are presented to demonstrate\u0000its benefit. The experiments show that the proposed approach is competitive\u0000across a range of problems, including both deceptive and non-deceptive problems","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small 评估开源稀疏自动编码器在 GPT-2 Small 中析取事实知识的能力

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-05 DOI: arxiv-2409.04478

Maheep Chaudhary, Atticus Geiger

引用次数: 0

Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications 免训练将预训练 ANN 转换为 SNN，以实现低功耗和高性能应用

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-05 DOI: arxiv-2409.03368

Tong Bu, Maohua Li, Zhaofei Yu

{"title":"Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications","authors":"Tong Bu, Maohua Li, Zhaofei Yu","doi":"arxiv-2409.03368","DOIUrl":"https://doi.org/arxiv-2409.03368","url":null,"abstract":"Spiking Neural Networks (SNNs) have emerged as a promising substitute for\u0000Artificial Neural Networks (ANNs) due to their advantages of fast inference and\u0000low power consumption. However, the lack of efficient training algorithms has\u0000hindered their widespread adoption. Existing supervised learning algorithms for\u0000SNNs require significantly more memory and time than their ANN counterparts.\u0000Even commonly used ANN-SNN conversion methods necessitate re-training of ANNs\u0000to enhance conversion efficiency, incurring additional computational costs. To\u0000address these challenges, we propose a novel training-free ANN-SNN conversion\u0000pipeline. Our approach directly converts pre-trained ANN models into\u0000high-performance SNNs without additional training. The conversion pipeline\u0000includes a local-learning-based threshold balancing algorithm, which enables\u0000efficient calculation of the optimal thresholds and fine-grained adjustment of\u0000threshold value by channel-wise scaling. We demonstrate the scalability of our\u0000framework across three typical computer vision tasks: image classification,\u0000semantic segmentation, and object detection. This showcases its applicability\u0000to both classification and regression tasks. Moreover, we have evaluated the\u0000energy consumption of the converted SNNs, demonstrating their superior\u0000low-power advantage compared to conventional ANNs. Our training-free algorithm\u0000outperforms existing methods, highlighting its practical applicability and\u0000efficiency. This approach simplifies the deployment of SNNs by leveraging\u0000open-source pre-trained ANN models and neuromorphic hardware, enabling fast,\u0000low-power inference with negligible performance reduction.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SNNAX -- Spiking Neural Networks in JAX SNNAX -- JAX 中的尖峰神经网络

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-04 DOI: arxiv-2409.02842

Jamie Lohoff, Jan Finkbeiner, Emre Neftci

引用次数: 0

Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network 利用尖峰神经网络实现遥感的脑启发式在线适应

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-03 DOI: arxiv-2409.02146

Dexin Duan, Peilin liu, Fei Wen

{"title":"Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network","authors":"Dexin Duan, Peilin liu, Fei Wen","doi":"arxiv-2409.02146","DOIUrl":"https://doi.org/arxiv-2409.02146","url":null,"abstract":"On-device computing, or edge computing, is becoming increasingly important\u0000for remote sensing, particularly in applications like deep network-based\u0000perception on on-orbit satellites and unmanned aerial vehicles (UAVs). In these\u0000scenarios, two brain-like capabilities are crucial for remote sensing models:\u0000(1) high energy efficiency, allowing the model to operate on edge devices with\u0000limited computing resources, and (2) online adaptation, enabling the model to\u0000quickly adapt to environmental variations, weather changes, and sensor drift.\u0000This work addresses these needs by proposing an online adaptation framework\u0000based on spiking neural networks (SNNs) for remote sensing. Starting with a\u0000pretrained SNN model, we design an efficient, unsupervised online adaptation\u0000algorithm, which adopts an approximation of the BPTT algorithm and only\u0000involves forward-in-time computation that significantly reduces the\u0000computational complexity of SNN adaptation learning. Besides, we propose an\u0000adaptive activation scaling scheme to boost online SNN adaptation performance,\u0000particularly in low time-steps. Furthermore, for the more challenging remote\u0000sensing detection task, we propose a confidence-based instance weighting\u0000scheme, which substantially improves adaptation performance in the detection\u0000task. To our knowledge, this work is the first to address the online adaptation\u0000of SNNs. Extensive experiments on seven benchmark datasets across\u0000classification, segmentation, and detection tasks demonstrate that our proposed\u0000method significantly outperforms existing domain adaptation and domain\u0000generalization approaches under varying weather conditions. The proposed method\u0000enables energy-efficient and fast online adaptation on edge devices, and has\u0000much potential in applications such as remote perception on on-orbit satellites\u0000and UAV.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification CoLaNET -- 用于分类的柱状分层结构尖峰神经网络

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-02 DOI: arxiv-2409.01230

Mikhail Kiselev

引用次数: 0

Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification 使用多输出混合回归和分类的景观感知自动算法配置

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-02 DOI: arxiv-2409.01446

Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein

{"title":"Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification","authors":"Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein","doi":"arxiv-2409.01446","DOIUrl":"https://doi.org/arxiv-2409.01446","url":null,"abstract":"In landscape-aware algorithm selection problem, the effectiveness of\u0000feature-based predictive models strongly depends on the representativeness of\u0000training data for practical applications. In this work, we investigate the\u0000potential of randomly generated functions (RGF) for the model training, which\u0000cover a much more diverse set of optimization problem classes compared to the\u0000widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly,\u0000we focus on automated algorithm configuration (AAC), that is, selecting the\u0000best suited algorithm and fine-tuning its hyperparameters based on the\u0000landscape features of problem instances. Precisely, we analyze the performance\u0000of dense neural network (NN) models in handling the multi-output mixed\u0000regression and classification tasks using different training data sets, such as\u0000RGF and many-affine BBOB (MA-BBOB) functions. Based on our results on the BBOB\u0000functions in 5d and 20d, near optimal configurations can be identified using\u0000the proposed approach, which can most of the time outperform the off-the-shelf\u0000default configuration considered by practitioners with limited knowledge about\u0000AAC. Furthermore, the predicted configurations are competitive against the\u0000single best solver in many cases. Overall, configurations with better\u0000performance can be best identified by using NN models trained on a combination\u0000of RGF and MA-BBOB functions.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JaxLife: An Open-Ended Agentic Simulator JaxLife：开放式代理模拟器

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-01 DOI: arxiv-2409.00853

Chris Lu, Michael Beukman, Michael Matthews, Jakob Foerster

引用次数: 0