Pub Date : 2023-02-01DOI: 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
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.
{"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}
Pub Date : 2023-01-20DOI: 10.48550/arXiv.2301.08665
Dominik Sobania, Martin Briesch, Philipp Rochner, Franz Rothlauf
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.
{"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}
Pub Date : 2022-11-28DOI: 10.48550/arXiv.2211.15621
Zhi-feng Zhou, Ziyu Qiu, Bradley Niblett, A. Johnston, J. Schwartzentruber, Nur Zincir-Heywood, M. Heywood
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.
{"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}
Pub Date : 2020-04-15DOI: 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}
Pub Date : 2020-04-15DOI: 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}
Pub Date : 2020-04-15DOI: 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}
Pub Date : 2020-04-15DOI: 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}
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