Definition
In computational contexts, evolvability refers to how easily an algorithm or neural network architecture can improve its fitness over generations or training steps. High evolvability implies that small changes in parameters or structure lead to significant, beneficial functional improvements. This concept is crucial in genetic algorithms and neuroevolution, where the search space must allow for progressive refinement of solutions without getting stuck in local optima.
Summary
The capacity of a genotype or system to generate heritable phenotypic variation that can be selected for adaptation.
Key Concepts
- Genetic Variation
- Adaptation Rate
- Search Space Structure
- Robustness
Use Cases
- Genetic algorithm optimization
- Neural architecture search
- Studying biological evolution simulations