Titlebook: Genetic Programming; 22nd European Confer Lukas Sekanina,Ting Hu,Pablo García-Sánchez Conference proceedings 2019 Springer Nature Switzerla

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Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functionsen when the former is well defined, the latter may not be obvious, e.g., in learning a strategy to navigate a maze to find a goal (objective), an effective objective function to . strategies may not be a simple function of the distance to the objective. We proposed to automate the means by which a g

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A Model of External Memory for Navigation in Partially Observable Visual Reinforcement Learning Tasktion takes the form of high-dimensional data, such as video. In addition, although the video might characterize a 3D world in high resolution, partial observability will place significant limits on what the agent can actually perceive of the world. This means that the agent also has to: (1) provide

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Fast DENSER: Efficient Deep NeuroEvolutionwhere we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of su

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A Vectorial Approach to Genetic Programmingpopular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a . representation where each observation corresponds to a collection on a subject at a

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0302-9743 19, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications...The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and?reflect the current