Evolutionary Neural Architecture Search (ENAS) automates the design of deep neural network architectures using evolutionary algorithms, addressing complex, multi-objective optimization problems such as balancing accuracy with computational efficiency. Key methods in this subfield of AutoML include Genetic Algorithms (GA) and Genetic Programming (GP) to traverse massive architectural search spaces. More information is available on IEEE Transactions on Evolutionary Computation.