Arkadiy Dushatskiy: Convolutional neural net surrogate-assisted GOMEA
Marco Virgolin: Linear Scaling with and within Semantic Backpropagation-based Genetic Programming for Symbolic Regression
Optimization problems with time-consuming objective function evaluations (expensive optimization) arise in different domains. We introduce a novel surrogate-assisted genetic algorithm for solving such optimization problems. The key novel features of our algorithm are keeping the strengths of the GOMEA algorithm while using a convolutional neural network as a surrogate model with a pairwise regression approach for model training to be able to train the CNN with small numbers of samples.
Both speakers will test-run their talks for the upcoming Genetic and Evolutionary Computation Conference (GECCO)
