Scalable Neuroevolution of Ensemble Learners
Marcel Merten, Rune Krauss, Rolf Drechsler · 2023
In recent years, machine learning has become increasingly important in daily life. One of the most popular machine learning models used in many applications is an Artificial Neural Network (ANN). While in applications such as automatic speech recognition there is sufficient knowledge about the expected behavior for each input to use supervised learning, other applications like robot control define only an overall target so that the expected output for a given input can be ambiguous, making supervised learning inapplicable. Therefore, Topology and Weight Evolving ANN (TWEANN) has been developed in the past to evolve ANN topologies and connection weights. However, challenges of TWEANN are the design of genetic recombination and the exploration of huge search spaces for suitable solutions induced in particular by large-scale problems which can lead to impractical runtimes.