Computer-implemented method for automatically generating an artificial neural network for the pixel-wise semantic segmentation of an image signal, comprising modules and connections linking these modules, wherein the artificial neural network generated in this way is configured to determine an associated output signal from an input signal, wherein the input signal is a two-dimensional image signal and wherein the output signal is a pixel-wise semantic segmentation of the input signal, wherein the method comprises the following steps: wherein successive modules and/or connections are added to a current starting network, wherein modules and/or connections to be added are randomly selected from a predefinable plurality of possible addable modules and connections, and wherein a plurality of possible developments of the current starting network are each generated by adding modules and/or connections to be added to the starting network, and wherein, when adding the respective modules and/or connections, predefinable parameters (p) that characterize the respective modules and/or connections to be added are selected such that the starting network to which the respective modules and/or connections to be added are added delivers unchanged output variables (y) for each possible input variable (x) in each case before and after the addition of the respective modules and/or connections to be added, and wherein one of the developments is then selected from the plurality of possible developments to serve as the current starting network in a next run of the method, wherein the current starting network that serves as the starting point for a first-time addition of modules and/or connections is trained with an initial training dataset before the first-time addition of modules and/or connections, and wherein the artificial neural network is trained to control an actuator based on sensor data. wherein the artificial neural network generated in this way is configured to determine an associated output signal from an input signal, wherein the input signal is a two-dimensional image signal and wherein the output signal is a pixel-wise semantic segmentation of the input signal, wherein the method comprises the following steps: wherein successive modules and/or connections are added to a current starting network, wherein modules and/or connections to be added are randomly selected from a predefinable plurality of possible addable modules and connections, and wherein a plurality of possible developments of the current starting network are each generated by adding modules and/or connections to be added to the starting network, and wherein, when adding the respective modules and/or connections, predefinable parameters (p) that characterize the respective modules and/or connections to be added are selected such that the starting network to which the respective modules and/or connections to be added are added delivers unchanged output variables (y) for each possible input variable (x) in each case before and after the addition of the respective modules and/or connections to be added, and wherein one of the developments is then selected from the plurality of possible developments to serve as the current starting network in a next run of the method, wherein the current starting network that serves as the starting point for a first-time addition of modules and/or connections is trained with an initial training dataset before the first-time addition of modules and/or connections, and wherein the artificial neural network is trained to control an actuator based on sensor data. wherein successive modules and/or connections are added to a current starting network, wherein modules and/or connections to be added are randomly selected from a predefinable plurality of possible addable modules and connections, and wherein a plurality of possible developments of the current starting network are each generated by adding modules and/or connections to be added to the starting network, and wherein, when adding the respective modules and/or connections, predefinable parameters (p) that characterize the respective modules and/or connections to be added are selected such that the starting network to which the respective modules and/or connections to be added are added delivers unchanged output variables (y) for each possible input variable (x) in each case before and after the addition of the respective modules and/or connections to be added, and wherein one of the developments is then selected from the plurality of possible developments to serve as the current starting network in a next run of the method, wherein the current starting network that serves as the starting point for a first-time addition of modules and/or connections is trained with an initial training dataset before the first-time addition of modules and/or connections, and wherein the artificial neural network is trained to control an actuator based on sensor data.