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The article analyzes the neuroevolution of the problematic issues ‒ a promising approach for solving complex problems of machine learning neural networks, adaptive management, and multi-agent systems, evolutionary robotics, search game strategies, computer art. The authors propose a neuroevolutionary algorithm that allows to "grow" a neural network for solving the problems of machine learning with reinforcement. The crossover operator is not used in the algorithm. The evolution of the network is performed due to slight mutational changes in a limited area. The advantages of the algorithm include its independence from the type of neuron activation functions, the absence of a training sample, and the ability to automatically find the appropriate neural network architecture. The authors demonstrate the results of benchmarking on the benchmark task, namely, the task of balancing a trolley with two flagpoles of different lengths. The simulation results support the hypothesis of the advantages of generation of neurostructures by small mutational changes in a limited area.

Benchmarking of neuroevolutionary approach for controlling task of trolley balance