Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set
In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to ...
Evolutionary approximation and neural architecture search
Automated neural architecture search (NAS) methods are now employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer’s effort. The NAS methods utilizing multi-objective ...
Applying genetic programming to PSB2: the next generation program synthesis benchmark suite
For the past seven years, researchers in genetic programming and other program synthesis disciplines have used the General Program Synthesis Benchmark Suite (PSB1) to benchmark many aspects of systems that conduct programming by example, where the ...
Severe damage recovery in evolving soft robots through differentiable programming
Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which locomoting robots are evolved and then given the ability ...
A grammar-based GP approach applied to the design of deep neural networks
Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks ...