seniorGenetic Algorithms
What is an adaptive genetic algorithm?
Updated May 16, 2026
Short answer
An adaptive GA dynamically adjusts parameters like mutation and crossover rates during evolution.
Deep explanation
Adaptive GA improves performance by changing internal parameters based on feedback from the search process. For example, if diversity decreases, mutation rate increases. This prevents stagnation and balances exploration and exploitation more effectively than static parameter settings.
Real-world example
Auto-tuning hyperparameters during neural network optimization.
Common mistakes
- Over-adjusting parameters too aggressively causing instability.
Follow-up questions
- What metrics are used for adaptation?
- Is adaptive GA always better?