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Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning 基于树的自动机器学习中词法选择的更快收敛
European Conference on Genetic Programming Pub Date : 2023-02-01 DOI: 10.48550/arXiv.2302.00731
Nicholas Matsumoto, A. Saini, Pedro Ribeiro, Hyun-Deok Choi, A. Orlenko, L. Lyytikainen, J. Laurikka, T. Lehtimaki, Sandra Batista, Jason W. Moore
{"title":"Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning","authors":"Nicholas Matsumoto, A. Saini, Pedro Ribeiro, Hyun-Deok Choi, A. Orlenko, L. Lyytikainen, J. Laurikka, T. Lehtimaki, Sandra Batista, Jason W. Moore","doi":"10.48550/arXiv.2302.00731","DOIUrl":"https://doi.org/10.48550/arXiv.2302.00731","url":null,"abstract":"In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121661573","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: 2
MTGP: Combining Metamorphic Testing and Genetic Programming MTGP:结合变形测试和遗传规划
European Conference on Genetic Programming Pub Date : 2023-01-20 DOI: 10.48550/arXiv.2301.08665
Dominik Sobania, Martin Briesch, Philipp Rochner, Franz Rothlauf
{"title":"MTGP: Combining Metamorphic Testing and Genetic Programming","authors":"Dominik Sobania, Martin Briesch, Philipp Rochner, Franz Rothlauf","doi":"10.48550/arXiv.2301.08665","DOIUrl":"https://doi.org/10.48550/arXiv.2301.08665","url":null,"abstract":"Genetic programming is an evolutionary approach known for its performance in program synthesis. However, it is not yet mature enough for a practical use in real-world software development, since usually many training cases are required to generate programs that generalize to unseen test cases. As in practice, the training cases have to be expensively hand-labeled by the user, we need an approach to check the program behavior with a lower number of training cases. Metamorphic testing needs no labeled input/output examples. Instead, the program is executed multiple times, first on a given (randomly generated) input, followed by related inputs to check whether certain user-defined relations between the observed outputs hold. In this work, we suggest MTGP, which combines metamorphic testing and genetic programming and study its performance and the generalizability of the generated programs. Further, we analyze how the generalizability depends on the number of given labeled training cases. We find that using metamorphic testing combined with labeled training cases leads to a higher generalization rate than the use of labeled training cases alone in almost all studied configurations. Consequently, we recommend researchers to use metamorphic testing in their systems if the labeling of the training data is expensive.","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115285708","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: 1
A Boosting Approach to Constructing an Ensemble Stack 构造集成堆栈的一种增强方法
European Conference on Genetic Programming Pub Date : 2022-11-28 DOI: 10.48550/arXiv.2211.15621
Zhi-feng Zhou, Ziyu Qiu, Bradley Niblett, A. Johnston, J. Schwartzentruber, Nur Zincir-Heywood, M. Heywood
{"title":"A Boosting Approach to Constructing an Ensemble Stack","authors":"Zhi-feng Zhou, Ziyu Qiu, Bradley Niblett, A. Johnston, J. Schwartzentruber, Nur Zincir-Heywood, M. Heywood","doi":"10.48550/arXiv.2211.15621","DOIUrl":"https://doi.org/10.48550/arXiv.2211.15621","url":null,"abstract":"An approach to evolutionary ensemble learning for classification is proposed in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116067796","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
Probabilistic Grammatical Evolution 概率语法演化
European Conference on Genetic Programming Pub Date : 2021-03-15 DOI: 10.1007/978-3-030-72812-0_13
Jessica M'egane, Nuno Lourenço, Penousal Machado
{"title":"Probabilistic Grammatical Evolution","authors":"Jessica M'egane, Nuno Lourenço, Penousal Machado","doi":"10.1007/978-3-030-72812-0_13","DOIUrl":"https://doi.org/10.1007/978-3-030-72812-0_13","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121352487","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: 9
Regenerating Soft Robots through Neural Cellular Automata 利用神经元胞自动机再生软体机器人
European Conference on Genetic Programming Pub Date : 2021-02-04 DOI: 10.1007/978-3-030-72812-0_3
Kazuya Horibe, Kathryn Walker, S. Risi
{"title":"Regenerating Soft Robots through Neural Cellular Automata","authors":"Kazuya Horibe, Kathryn Walker, S. Risi","doi":"10.1007/978-3-030-72812-0_3","DOIUrl":"https://doi.org/10.1007/978-3-030-72812-0_3","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133122402","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: 21
Mining Feature Relationships in Data 挖掘数据中的特征关系
European Conference on Genetic Programming Pub Date : 2021-02-02 DOI: 10.1007/978-3-030-72812-0_16
Andrew Lensen
{"title":"Mining Feature Relationships in Data","authors":"Andrew Lensen","doi":"10.1007/978-3-030-72812-0_16","DOIUrl":"https://doi.org/10.1007/978-3-030-72812-0_16","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130281454","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: 3
Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling 动态柔性作业车间调度遗传规划中遗传算子的引导子树选择
European Conference on Genetic Programming Pub Date : 2020-04-15 DOI: 10.1007/978-3-030-44094-7_17
Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
{"title":"Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling","authors":"Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang","doi":"10.1007/978-3-030-44094-7_17","DOIUrl":"https://doi.org/10.1007/978-3-030-44094-7_17","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"11 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133855201","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: 15
Automatically Evolving Lookup Tables for Function Approximation 自动演化查找表的函数逼近
European Conference on Genetic Programming Pub Date : 2020-04-15 DOI: 10.1007/978-3-030-44094-7_6
Oliver Krauss, W. Langdon
{"title":"Automatically Evolving Lookup Tables for Function Approximation","authors":"Oliver Krauss, W. Langdon","doi":"10.1007/978-3-030-44094-7_6","DOIUrl":"https://doi.org/10.1007/978-3-030-44094-7_6","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128611661","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: 7
Classification of Autism Genes Using Network Science and Linear Genetic Programming 基于网络科学和线性遗传规划的自闭症基因分类
European Conference on Genetic Programming Pub Date : 2020-04-15 DOI: 10.1007/978-3-030-44094-7_18
Yu Zhang, Y. Chen, Ting Hu
{"title":"Classification of Autism Genes Using Network Science and Linear Genetic Programming","authors":"Yu Zhang, Y. Chen, Ting Hu","doi":"10.1007/978-3-030-44094-7_18","DOIUrl":"https://doi.org/10.1007/978-3-030-44094-7_18","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125794372","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: 2
Effect of Parent Selection Methods on Modularity 亲本选择方法对模块化的影响
European Conference on Genetic Programming Pub Date : 2020-04-15 DOI: 10.1007/978-3-030-44094-7_12
A. Saini, L. Spector
{"title":"Effect of Parent Selection Methods on Modularity","authors":"A. Saini, L. Spector","doi":"10.1007/978-3-030-44094-7_12","DOIUrl":"https://doi.org/10.1007/978-3-030-44094-7_12","url":null,"abstract":"","PeriodicalId":206738,"journal":{"name":"European Conference on Genetic Programming","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648040","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: 6
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