Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS

Source: Dev.to

Introduction

Which method finds better neural networks—slow search, random tries, or evolutionary picks? We examined three simple approaches for searching neural architecture designs and observed clear differences.

Grid Search

Grid Search checks many fixed options and can be slow, but sometimes it finds steady results. It exhaustively evaluates a predefined set of hyperparameter combinations.

Random Search

Random Search tries many random choices and often finds good settings faster than expected. By sampling the search space uniformly, it can locate promising configurations with fewer evaluations.

Genetic Algorithm

A Genetic Algorithm mixes and mutates designs over time. It can discover clever architectures but typically requires more runs to demonstrate its advantage. The evolutionary process iteratively selects, recombines, and mutates candidate networks.

Experimental Setup

The tests were run on a common image dataset, so the results reflect real tasks that practitioners care about. Accuracy was used as the primary metric, while runtime measured the computational cost of each method.

Comparison

• Speed vs. Accuracy: Random Search often provides quick answers with reasonable accuracy. Grid Search is thorough but slower, and may yield consistent but not necessarily superior models. Genetic Algorithms can achieve higher accuracy given enough time and evaluations.
• Resource Considerations: If you have limited compute, Random Search is a practical choice. When you can afford longer runtimes, the evolutionary approach may produce the best models. Grid Search suits scenarios where exhaustive coverage of a small search space is desired.

Conclusion

Each method has pros and cons. Choose the approach that fits your goal and compute budget, and consider trying multiple strategies because results can be surprising.

Further reading

Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS