evolutionary architecture search

**Evolutionary Architecture Search** is a **NAS method that uses evolutionary algorithms — selection, crossover, and mutation — to evolve neural network architectures over generations** — maintaining a population of candidate architectures and iteratively improving them through biologically-inspired operations. **How Does Evolutionary NAS Work?** - **Population**: Initialize a set of random architectures. - **Fitness**: Train each architecture and evaluate accuracy (and optionally latency/size). - **Selection**: Keep the fittest architectures. Remove the worst. - **Mutation**: Randomly modify operations, connections, or hyperparameters. - **Crossover**: Combine parts of two parent architectures to create children. - **Examples**: AmoebaNet, NEAT, Large-Scale Evolution (Real et al., 2019). **Why It Matters** - **No Gradient Required**: Works for non-differentiable search spaces and objectives. - **Exploration**: Better at exploring diverse regions of the search space than gradient-based methods. - **Quality**: AmoebaNet achieved state-of-the-art ImageNet accuracy, matching RL-based NASNet. **Evolutionary NAS** is **natural selection for neural networks** — breeding and evolving architectures over generations until the fittest designs emerge.