Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献_第2页

Bayesian Optimization 贝叶斯优化

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595059

Ivo Couckuy, Sebastian Rojas-Gonzalez, Juergen Branke, B. Bischl

引用次数: 0

Accelerating Gene-pool Optimal Mixing Evolutionary Algorithm for Neural Architecture Search with Synaptic Flow 基于突触流的神经结构搜索加速基因库最优混合进化算法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596438

Khoa Huu Tran, Luc Truong, An Vo, N. H. Luong

引用次数: 0

Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction 通过可行性预测提高课程排课人工实例的相关性

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590690

Thomas Feutrier, Nadarajen Veerapen, Marie-Éléonore Kessaci

{"title":"Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction","authors":"Thomas Feutrier, Nadarajen Veerapen, Marie-Éléonore Kessaci","doi":"10.1145/3583133.3590690","DOIUrl":"https://doi.org/10.1145/3583133.3590690","url":null,"abstract":"Solvers for Curriculum-Based Course Timetabling were until recently difficult to configure and evaluate because of the limited number of benchmark instances. Recent work has proposed new real-world instances, as well as thousands of generated ones that can be used to train configurators and for machine learning applications. The less numerous real-world instances can then be used as a test set. To assess whether the generated instances exhibit sufficiently similar behavior to the real ones, we choose to consider a basic indicator: feasibility. We find that 38 % of the artificial instances are infeasible versus 6% of real-world ones, and show that a feasibility prediction model trained on artificial instances performs extremely poorly on real-world ones. The objective of this paper is therefore to be able to predict which generated instances behave like the real-world instances in order to improve the quality of the training set. As a first step, we propose a selection procedure for the artificial training set that produces a feasibility prediction model that works as well as if it were trained on real-world instances. Then, we propose a pipeline to build a selection model that picks artificial instances that match the infeasibility behavior of the real-world ones.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"1 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":"128800239","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

An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware 一种基于进化的生成对抗网络启发方法来击败变形恶意软件

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596362

Kehinde O. Babaagba, Jordan Wylie

{"title":"An Evolutionary based Generative Adversarial Network Inspired Approach to Defeating Metamorphic Malware","authors":"Kehinde O. Babaagba, Jordan Wylie","doi":"10.1145/3583133.3596362","DOIUrl":"https://doi.org/10.1145/3583133.3596362","url":null,"abstract":"Defeating dangerous families of malware like polymorphic and metamorphic malware have become well studied due to their increased attacks on computer systems and network. Traditional Machine Learning (ML) models have been used in detecting this malware, however they are often not resistant to future attacks. In this paper, an Evolutionary based Generative Adversarial Network (GAN) inspired approach is proposed as a step towards defeating metamorphic malware. This method uses an Evolutionary Algorithm as a generator to create malware that are designed to fool a detector, a deep learning model into classifying them as benign. We employ a personal information stealing malware family (Dougalek) as a testbed, selected based on its malicious payload and evaluate the samples generated based on their adversarial accuracy, measured based on the number of Antivirus (AV) engines they are able to fool and their ability to fool a set of ML detectors (k-Nearest Neighbors algorithm, Support Vector Machine, Decision Trees, and Multi-Layer Perceptron). The results show that the adversarial samples are on average able to fool 63% of the AV engines and the ML detectors are susceptible to the new mutants achieving an accuracy between 60%-77%.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"1 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":"128731246","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

A Novel Fitness Function for Automated Software Test Case Generation Based on Nested Constraint Hardness 一种基于嵌套约束硬度的软件测试用例自动生成适应度函数

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590727

Thi-Mai-Anh Bui, Q. Bui, Van-Tri Do

{"title":"A Novel Fitness Function for Automated Software Test Case Generation Based on Nested Constraint Hardness","authors":"Thi-Mai-Anh Bui, Q. Bui, Van-Tri Do","doi":"10.1145/3583133.3590727","DOIUrl":"https://doi.org/10.1145/3583133.3590727","url":null,"abstract":"Search-Based Software Testing (SBST) has drawn a lot of interests as a powerful approach for automated test data generation. One major limitation of search-based methods is that they may get stuck in local optima and become inefficient if the fitness function does not provide any direction toward a test target, particularly when dealing with hard branch targets (e.g., nested predicates). Recent research has focused on enhancing the fitness function by taking branch hardness into account in order to direct the evolutionary process. However, either the characteristics of constraints (e.g., their involved variables' domain) are not taken into account or the difficulty level of a branch is separately studied. In this paper, we aim to address the test data generation with a focus on hard branches by proposing a novel fitness function based on nested constraints (i.e., including the target constraint and those that impact its coverage) and the domain sizes of their variables. The empirical study promises efficiency and effectiveness for the new fitness function. Our proposed approach outperforms its counterparts significantly, particularly for the branches that are difficult to be covered.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"3 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":"126884045","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

Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation 基于安全多方计算的垂直保护隐私符号回归

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596337

Du Nguyen Duy, M. Affenzeller, R. Nikzad‐Langerodi

{"title":"Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation","authors":"Du Nguyen Duy, M. Affenzeller, R. Nikzad‐Langerodi","doi":"10.1145/3583133.3596337","DOIUrl":"https://doi.org/10.1145/3583133.3596337","url":null,"abstract":"Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. Due to its efficiency and flexibility, Genetic Programming can be seen as the standard search technique for Symbolic Regression. However, the conventional Genetic Programming algorithm requires storing all data in a central location, which is not always feasible due to growing concerns about data privacy and security. While privacy-preserving research has advanced recently and might offer a solution to this problem, their application to Symbolic Regression remains largely unexplored. Furthermore, the existing work only focuses on the horizontally partitioned setting, whereas the vertically partitioned setting, another popular scenario, has yet to be investigated. Herein, we propose an approach that employs a privacy-preserving technique called Secure Multiparty Computation to enable parties to jointly build Symbolic Regression models in the vertical scenario without revealing private data. Preliminary experimental results indicate that our proposed method delivers comparable performance to the centralized solution while safeguarding data privacy.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"1 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":"129094241","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

User-centred Design and Development of a Graphical User Interface for Learning Classifier Systems 以用户为中心的学习分类器系统图形用户界面的设计与开发

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596357

Sooraj K Babu, Tim Schneider, S. von Mammen

引用次数: 0

Metaheuristic Optimization for Transmission Network Expansion Planning: Testebed 2 of the Competition on Evolutionary Computation in the Energy Domain 输电网扩展规划的元启发式优化:能量域演化计算竞争的实验平台2

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596347

José Almeida, F. Lezama, J. Soares, Leonardo H. Macedo, Z. Vale, Rubén R. Romero

{"title":"Metaheuristic Optimization for Transmission Network Expansion Planning: Testebed 2 of the Competition on Evolutionary Computation in the Energy Domain","authors":"José Almeida, F. Lezama, J. Soares, Leonardo H. Macedo, Z. Vale, Rubén R. Romero","doi":"10.1145/3583133.3596347","DOIUrl":"https://doi.org/10.1145/3583133.3596347","url":null,"abstract":"The complexity of the transmission network expansion planning (TNEP) problem has been increasing due to the new constraints given by renewable generation uncertainty, new market rules and players, and the continuous demand growth with the introduction of electric vehicles and energy storage systems. The problem consists of finding the optimal number and location of new transmission lines to support the demand, which can be extremely hard to optimize. As such, in this paper, we focus on metaheuristic optimization to solve a TENP problem proposed in testbed 2 of the 2023 competition on evolutionary computation in the energy domain. The 87-bus north-northeast Brazilian transmission system is considered for the case study, and different DE metaheuristics are used for the optimization process. Results show that the HyDE algorithm presents the overall best performance when compared to other DE strategies. HyDE is able to achieve the overall lowest costs with a reduction of around 67% compared to L-SHADE.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"1 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":"130629801","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

Maelstrom: An Accelerator-compatible GP Framework 漩涡:一个加速器兼容的GP框架

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596359

Deacon Seals, Robert Wilkes, D. Tauritz

引用次数: 0

Absumption and Subsumption based Learning Classifier System for Real-World Continuous-based Problems 现实世界连续问题的基于吸收和包容的学习分类器系统

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590564

Yi Liu, Yu Cui, Will N. Browne, Bing Xue, Wen Cheng, Yong Li, Lingfang Zeng

引用次数: 0