Transfer learning in constructive induction with Genetic Programming


Transfer learning (TL) is the process by which some aspects of a machine learning model generated on a source task is transferred to a target task, to simplify the learning required to solve the target. TL in Genetic Programming (GP) has not received much attention, since it is normally assumed that an evolved symbolic expression is specifically tailored to a problem’s data and thus cannot be used in other problems. The goal of this work is to present a broad and diverse study of TL in GP, considering a varied set of source and target tasks, and dealing with questions that have received little, or no attention, in previous GP literature. In particular, this work studies the performance of transferred solutions when the source and target tasks are from different domains, and when they do not share a similar input feature space. Additionally, the relationship between the success and failure of transferred solutions is studied, considering different source and target tasks. Finally, the predictability of TL performance is analyzed for the first time in GP literature. GP-based constructive induction of features is used to carry out the study, a wrapper-based approach where GP is used to construct feature transformations and an additional learning algorithm is used to fit the final model. The experimental work presents several notable results and contributions. First, TL is capable of generating solutions that outperform, in many cases, baseline methods in classification and regression tasks. Second, it is shown that some problems are good source problems while others are good targets in a TL system. Third, the transferability of solutions is not necessarily symmetric between two problems. Finally, results show that it is possible to predict the success of TL in some cases, particularly in classification tasks.

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  1. 1.

    The terms original features, raw features and problem features are used to denote the input features (independent variables) that are contained in a machine learning dataset. These terms are used interchangeably in the rest of this paper.

  2. 2.

    We do not provide a complete description of all the works in this area, such a survey is beyond the scope of this paper. Instead, we chose representative papers of the groups and subgroups in our taxonomy of research in this area. We focused on unique papers that illustrate the main features of each group, and included works that have also achieved good performance on real-world tasks.

  3. 3.

  4. 4.

    These results do not imply that source task features are not important in predicting TL performance, only that target task features are more important for a Random Forest regression model.

  5. 5.

    All of the predictors are plotted in a [0, 1] scale. For predictors with unbounded domains, min-max normalization was used.


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This research was funded by CONACYT (Mexico) Fronteras de la Ciencia 2015-2 Project No. FC-2015-2/944, and first author was supported by CONACYT graduate scholarship No. 302526. This work was also partially supported by FCT through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PERSEIDS (PTDC/EMS-SIS/0642/2014), INTERPHENO (PTDC/ASP-PLA/28726/2017), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), PREDICT (PTDC/CCI-CIF/29877/2017) and GADgET (DSAIPA/DS/0022/2018). The authors also thank Mauro Castelli from NOVA IMS for suggesting important references on transfer learning with GP.

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Muñoz, L., Trujillo, L. & Silva, S. Transfer learning in constructive induction with Genetic Programming. Genet Program Evolvable Mach 21, 529–569 (2020).

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  • Transfer learning
  • Constructive induction of features
  • Genetic Programming