The article discusses the essence of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm in solving problems of neural network optimization and evolution of their topologies. An overview of the current state of use of NEAT and its adaptations in evolutionary computing research is given. The need for the Quality Diversity (QD) approach to increase the diversity and quality of solutions in complex problems is substantiated. The QD concept and its impact on the search for innovative solutions within diverse search spaces are described. The application of ViE-NEAT, which combines the advantages of survival of the fittest solutions with the principle of maintaining diversity, is described. The main aspects of ViE-NEAT are compared with the traditional NEAT, analyzing the advantages of using the survival algorithm in comparison with competitive methods. A detailed description of the MAP-Elites algorithm is given, which demonstrates an alternative approach to finding solutions by ensuring the diversity of the “illuminated” feature space, which can be integrated with NEAT to form a more diversified population of solutions. The main focus is on the methodology of integrating NEAT with MAP-Elites algorithms to create an adapted search strategy. Based on the basic principles of NEAT, the possibilities of its extension for effective solution of new problems that are not limited to traditional parametric spaces are determined. An analysis of the results demonstrating the efficiency of using the adapted NEAT algorithm in comparison with traditional approaches to the evolution of neural networks is presented.
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