Machine Learning / Artificial Evolution, Genetic Programming

Chair: Conor Ryan

This track aims at gathering scientists and practitioners interested in Machine Learning / Artificial Evolution, Genetic Programming from the point of view of Complex Systems.

Submissions of posters (3 pages LNCS format), short papers (6 pages LNCS format) or full papers (12 pages LNCS format) are encouraged on topics including (but not limited to): Adaptive systems; Bioinformatics; Pattern Recognition; Neural Networks; Evolutionary Computation; Artificial Life; Grammatical Evolution; Genetic Programming; Fuzzy Sets; Ensembles; Deep Learning; Clustering; Reinforcement Learning; Time Series Analysis; Bayesian Methods; Probabilistic Inference; Monte Carlo methods; Trustworthy Machine Learning; Applications of Machine Learning.

List of reviewers: Douglas Mota Dias, Meghana Kshirsagar, Darian Reyes, Pierre Collet, Conor Ryan, Enrique Naredo, Ayman Mahgoub, Maarten Keijzer, Gopinath Chennupati, Ting Hu, Smitaa Kasar, James Patten, Lukas Rosenauer, Ernesto Tarantino, Leonardo Trujillo, Mario Giacobini, Stefano Cagnoni, Malcolm Heywood, Penousal Machado, Tomasz Pawlak, Eric Medvet, Ignacio Hidalgo, Nuno Lourenco, Stjepan Piceck

Submissions and specific reviewing procedure