seniorGenetic Algorithms
What is estimation of distribution algorithm (EDA) vs GA?
Updated May 16, 2026
Short answer
EDA replaces crossover/mutation with probabilistic model learning.
Deep explanation
Unlike GA, EDAs build a probabilistic model of promising solutions and sample new individuals from it. This removes explicit crossover and mutation and instead focuses on learning variable dependencies.
Real-world example
Learning feature distributions in automated machine learning systems.
Common mistakes
- Assuming EDAs still use crossover like GA.
Follow-up questions
- Why use EDAs?
- What is downside?