Adaptation of the Neat Algorithm for Complex Problems With the Help of Quality Diversity Algorithms
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.