Genetic algorithms
Practice Flashcards
Flip to reveal answersWhat are the stages of a genetic algorithm?
Track your progress — Sign up free to save your progress and get smart review reminders based on spaced repetition.
All 5 Flashcards — Genetic algorithms
Sign up free to track progress and get spaced-repetition review schedules.
Question
What are the stages of a genetic algorithm?
Answer
**Population → fitness → selection → crossover → mutation → repeat.**
Question
When is a genetic algorithm appropriate?
Answer
When the search space is **too large to enumerate**, no formula gives the answer, candidates can be **scored**, and "good enough" is acceptable.
Question
Crossover or mutation — which introduces novelty?
Answer
**Mutation.** Crossover only recombines values already in the population; mutation can produce one present in neither parent.
Question
What happens if the mutation rate is too high?
Answer
It becomes a **random search** — good solutions are destroyed as fast as they are found.
Question
Does a genetic algorithm find the optimal solution?
Answer
**No** — a good one. There is no optimality guarantee, and two runs can give different answers.
Read the notes
Full study notes for Genetic algorithms
Topic 4.3 hub
Machine learning approaches
More from Topic 4.3
All flashcards in this topic
Computer Science exam skills
Paper structures & tips
Track your progress with spaced repetition
Sign up free — Aimnova tells you exactly which cards to review and when, so you remember everything before your IB exam.
Start Free