Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献_第4页

Automated algorithm composition of unsupervised image clustering algorithms 自动算法组成的无监督图像聚类算法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590555

Mia Gerber, N. Pillay

引用次数: 0

Hot off the Press: Runtime Analysis for the NSGA-II - Provable Speed-Ups From Crossover 新闻热点：NSGA-II 的运行时间分析--可证明的交叉速度提升

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595845

Benjamin Doerr, Zhongdi Qu

引用次数: 0

Optimising Ferroelectric Thin Films with Evolutionary Computation 用进化计算优化铁电薄膜

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590750

E. Vissol-Gaudin, Y. Lim, K. Hippalgaonkar

{"title":"Optimising Ferroelectric Thin Films with Evolutionary Computation","authors":"E. Vissol-Gaudin, Y. Lim, K. Hippalgaonkar","doi":"10.1145/3583133.3590750","DOIUrl":"https://doi.org/10.1145/3583133.3590750","url":null,"abstract":"This paper presents the integration of machine learning and image analysis techniques into a material science experimental workflow. The aim is to optimise the properties of an Aluminium Scandium Nitride thin film through the manipulation of experimental input parameters. This is formulated as an optimisation problem, were the search space consists in the set of experimental input parameters used during the film's synthesis. The solution's fitness is obtained through the analysis of Scanning-Electron-Microscopy images and corresponds to the surface defect density over a film. An optimum solution to this problem is defined as the set of input parameters that consistently produces a film with no measurable surface defects. The search space is a black box with possibly more than one optimum and the limited amount of experiments that can be undertaken make efficient exploration challenging. It is shown that classification can be used to reduce the problem's search space by identifying areas of infeasibility. Using nested cross-validation, tree-based classifiers emerge as the most accurate, and importantly, interpretable algorithms for this task. Subsequently, Particle Swarm Optimisation is used to find optimal solutions to the surface defect minimisation problem. Preliminary experimental results show a significant decrease in defect density average achieved.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115232655","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

Diversity Search for the Generation of Diverse Grasping Trajectories 不同抓取轨迹生成的多样性搜索

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590718

J. Huber, Oumar Sane, Miranda Coninx, F. Ben Amar, S. Doncieux

引用次数: 1

A Dynamic Partial Update for Covariance Matrix Adaptation 协方差矩阵自适应的动态局部更新

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596385

Hiroki Shimizu, Masashi Toyoda

{"title":"A Dynamic Partial Update for Covariance Matrix Adaptation","authors":"Hiroki Shimizu, Masashi Toyoda","doi":"10.1145/3583133.3596385","DOIUrl":"https://doi.org/10.1145/3583133.3596385","url":null,"abstract":"Tackling large-scale and ill-conditioned problems is demanding even for the covariance matrix adaptation evolution strategy (CMA-ES), which is a state-of-the-art algorithm for black-box optimization. The coordinate selection is a technique that mitigates the ill-conditionality of large-scale problems by updating parameters in partially selected coordinate spaces. This technique can be applied to various CMA-ES variants and improves their performance especially for ill-conditioned problems. However, it often fails to improve the performance of well-conditioned problems, because it is difficult to choose appropriate coordinate spaces according to the ill-conditionality of problems. We introduce a dynamic partial update method for coordinate selection to solve the above problem. We use the second-order partial derivatives of an objective function to estimate the condition number and select coordinates so that the condition number of each pair does not exceed the given allowable value. In this method, the number of clusters becomes to be small for well-conditioned problems and large for ill-conditioned cases. In particular, the selection does not execute if the condition number of the full space is less than the allowable value. We observe significant improvements in well-conditioned problems and comparable performances in ill-conditioned cases in numerical experiments.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114625070","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

Extending MOEA/D to Constrained Multi-objective Optimization via Making Constraints an Objective Function 将约束作为目标函数将MOEA/D扩展到约束多目标优化

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590583

Y. Yasuda, K. Tamura, K. Yasuda

引用次数: 0

An Investigation of Terminal Settings on Multitask Multi-objective Dynamic Flexible Job Shop Scheduling with Genetic Programming 基于遗传规划的多任务多目标动态柔性作业车间调度终端设置研究

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590546

Fangfang Zhang, Yi Mei, Mengjie Zhang

{"title":"An Investigation of Terminal Settings on Multitask Multi-objective Dynamic Flexible Job Shop Scheduling with Genetic Programming","authors":"Fangfang Zhang, Yi Mei, Mengjie Zhang","doi":"10.1145/3583133.3590546","DOIUrl":"https://doi.org/10.1145/3583133.3590546","url":null,"abstract":"Multitask learning has attracted widespread attention to handle multiple tasks simultaneously. Multitask genetic programming has been successfully used to learn scheduling heuristics for multiple multi-objective dynamic flexible job shop scheduling tasks simultaneously. With genetic programming, the learned scheduling heuristics consist of terminals that are extracted from the features of specific tasks. However, how to set proper terminals with multiple tasks still needs to be investigated. This paper has investigated the effectiveness of three strategies for this purpose, i.e., intersection strategy to use the common terminals between tasks, separation strategy to apply different terminals for different tasks, and union strategy to utilise all the terminals needed for all tasks. The results show that the union strategy which gives tasks the terminals needed by all tasks performs the best. In addition, we find that the learned routing/sequencing rule by the developed algorithm with union strategy in one multitask scenario can share knowledge between each other. On the other hand and more importantly, the learned routing/sequencing rule can also be specific to their tasks with distinguished knowledge represented by genetic materials.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123472994","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

EvoPrunerPool: An Evolutionary Pruner using Pruner Pool for Compressing Convolutional Neural Networks EvoPrunerPool:一个使用修剪池来压缩卷积神经网络的进化修剪器

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596333

Shunmuga Velayutham C., Sujit Subramanian S, A. K, M. Sathya, Nathiyaa Sengodan, Divesh Kosuri, Sai Satvik Arvapalli, Thangavelu S, J. G

{"title":"EvoPrunerPool: An Evolutionary Pruner using Pruner Pool for Compressing Convolutional Neural Networks","authors":"Shunmuga Velayutham C., Sujit Subramanian S, A. K, M. Sathya, Nathiyaa Sengodan, Divesh Kosuri, Sai Satvik Arvapalli, Thangavelu S, J. G","doi":"10.1145/3583133.3596333","DOIUrl":"https://doi.org/10.1145/3583133.3596333","url":null,"abstract":"This paper proposes EvoPrunerPool - an Evolutionary Pruner using Pruner Pool for Compressing Convolutional Neural Networks. EvoPrunerPool formulates filter pruning as a search problem for identifying the right set of pruners from a pool of off-the-shelf filter pruners and applying them in appropriate sequence to incrementally sparsify a given Convolutional Neural Network. The efficacy of EvoPrunerPool has been demonstrated on LeNet model using MNIST data as well as on VGG-19 deep model using CIFAR-10 data and its performance has been benchmarked against state-of-the-art model compression approaches. Experiments demonstrate a very competitive and effective performance of the proposed Evolutionary Pruner. Since EvoPrunerPool employs the native representation of a popular machine learning framework and filter pruners from a well-known AutoML toolkit the proposed approach is both extensible and generic. Consequently, a typical practitioner can use EvoPrunerPool without any in-depth understanding of filter pruning in specific and model compression in general.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125852540","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

Does size matter? On the influence of ensemble size on constructing ensembles of dispatching rules 大小重要吗?集成规模对构建调度规则集成的影响

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590562

Marko Durasevic, F. Gil-Gala, D. Jakobović

{"title":"Does size matter? On the influence of ensemble size on constructing ensembles of dispatching rules","authors":"Marko Durasevic, F. Gil-Gala, D. Jakobović","doi":"10.1145/3583133.3590562","DOIUrl":"https://doi.org/10.1145/3583133.3590562","url":null,"abstract":"Recent years saw an increase in the application of genetic programming (GP) as a hyper-heuristic, i.e., a method used to generate heuristics for solving various combinatorial optimisation problems. One of its widest application is in scheduling to automatically design constructive heuristics called dispatching rules (DRs). DRs are crucial for solving dynamic scheduling environments, in which the conditions change over time. Although automatically designed DRs achieve good results, their performance is limited as a single DR cannot always perform well. Therefore, various methods were used to improve their performance, among which ensemble learning represents one of the most promising directions. Using ensembles introduces several new parameters, such as the ensemble construction method, ensemble collaboration method, and ensemble size. This study investigates the possibility to remove the ensemble size parameter when constructing ensembles. Therefore, the simple ensemble combination method is adapted to randomly select the size of the ensemble it generates, rather than using a fixed ensemble size. Experimental results demonstrate that not using a fixed ensemble size does not result in a worse performance, and that the best ensembles are of smaller sizes. This shows that the ensemble size can be eliminated without a significant influence on the performance.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126190675","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

Evolutionary Pruning of Deep Convolutional Networks by a Memetic GA with Sped-Up Local Optimization and GLCM Energy Z-Score 基于加速局部优化和GLCM能量Z-Score的Memetic GA深度卷积网络进化剪枝

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590604

Hana Cho, Han Joon Byun, Min Kee Kim, Joon Huh, Byung-Ro Moon

引用次数: 0