Mafalda Malafaia & Thalea Schlender
Title: Modelling discontinuities in GP-GOMEA
Abstract:
Often it can be easier to capture real-world relationships by considering that a phenomenon may be described by multiple relationships across its input space. As such a categorical variable may induce different subsolutions. In a medical application, this could, for instance, imitate the notion of segregating patients that doctors make use of. Then, different relationships are established, depending on which cluster a patient belongs to. The original GP-GOMEA has difficulty modelling these relationships.
Our work, therefore, adds the capability of utilising both categorical and numeric features, as well as the capability to model these discontinuities. Specifically, we do this by adding if statements and syntactical constraints. Although this increases the branching factor of the tree from 2 to 3, the resulting piece-wise relationships are often more understandable. Nonetheless, our work also aims to enhance the variation to improve search efficiency.
This work is currently in progress.
Title: Multi-Modal Pipeline
Abstract:
Many real-world problems give pieces of data in different modalities. A medical application, for instance, may have valuable demographic and health characteristics, such as age or weight. Next to this, however, it may also provide essential information via imaging in the form of MRI or CT scans. To utilise the most information, these sources must be considered together. We, thus, aim to build an image feature-engineering approach, which can take other modalities into consideration. In combination with tabular data such image features may then be used to evolve explainable semantic regression models.
Specifically, our work tests various neural network configurations that fuse different modalities together in either an early or intermediate stage. Moreover, we consider the use of unsupervised pretraining of parts of the network.
This work is currently in progress.
