seniorGenetic Algorithms
What is a deceptive fitness function in Genetic Algorithms?
Updated May 16, 2026
Short answer
A deceptive fitness function misleads GA toward suboptimal solutions.
Deep explanation
Deceptive functions are designed such that local improvements lead away from the global optimum. They violate the building block principle by making low-order schemas misleading. GAs struggle because selection favors locally optimal but globally incorrect regions.
Real-world example
Feature selection where individually strong features perform poorly together.
Common mistakes
- Assuming greedy improvement leads to global optimum.
Follow-up questions
- How do GAs handle deception?
- What is a trap function?