seniorGenetic Algorithms
What is steady-state Genetic Algorithm?
Updated May 16, 2026
Short answer
A steady-state GA replaces a few individuals per iteration instead of full generational replacement.
Deep explanation
Unlike generational GA where the entire population is replaced, steady-state GA continuously updates the population by inserting new offspring and removing the worst or random individuals. This leads to smoother convergence and often faster real-time optimization.
Real-world example
Real-time routing systems where solutions must continuously improve without full recomputation.
Common mistakes
- Assuming steady-state always converges faster
- it may get stuck in local optima.
Follow-up questions
- How does it differ from generational GA?
- When is it preferred?