Computer-implemented method for determining a suitable network configuration for a neural network for calculation hardware and for a predefined application, in particular for implementing functions of a technical system, in particular a robot, a vehicle, a tool or a factory machine, wherein the predefined application is determined in the form of training data; wherein the network configuration indicates the architecture of the neural network, said method having the following steps of: a) providing a current network configuration set having network configurations, wherein the current network configuration set corresponds to a Pareto set in terms of a prediction error and at least one further optimization goal, wherein the further optimization goal characterizes resource costs of the network configuration on the calculation hardware, wherein the resource costs are a memory size, evaluation speed, compatibility in terms of special hardware or an evaluation energy consumption; b) providing a set of network configuration variants on the basis of variations of the network configurations in the current network configuration set; c) selecting a subset of network configurations from the set of provided network configuration variants on the basis of a probability distribution (p), wherein the probability distribution (p) characterizes a distribution of the current network configuration set, in particular Pareto set, in terms of the at least one further optimization goal; d) training neural networks of each of the selected network configurations and determining a corresponding prediction error (error); e) updating the current network configuration set on the basis of the prediction errors and the at least one further optimization goal of the network configuration set and the selected network configurations; and f) selecting the suitable network configuration from the current network configuration set, wherein the suitable network configuration is selected from the current network configuration set on the basis of a total cost function which depends on the prediction error and the resource costs. a) providing a current network configuration set having network configurations, wherein the current network configuration set corresponds to a Pareto set in terms of a prediction error and at least one further optimization goal, wherein the further optimization goal characterizes resource costs of the network configuration on the calculation hardware, wherein the resource costs are a memory size, evaluation speed, compatibility in terms of special hardware or an evaluation energy consumption; b) providing a set of network configuration variants on the basis of variations of the network configurations in the current network configuration set; c) selecting a subset of network configurations from the set of provided network configuration variants on the basis of a probability distribution (p), wherein the probability distribution (p) characterizes a distribution of the current network configuration set, in particular Pareto set, in terms of the at least one further optimization goal; d) training neural networks of each of the selected network configurations and determining a corresponding prediction error (error); e) updating the current network configuration set on the basis of the prediction errors and the at least one further optimization goal of the network configuration set and the selected network configurations; and f) selecting the suitable network configuration from the current network configuration set, wherein the suitable network configuration is selected from the current network configuration set on the basis of a total cost function which depends on the prediction error and the resource costs.