The big idea: A genetic algorithm searches for a good solution by imitating evolution: keep a population of candidates, score them, let the best ones combine, mutate a few, repeat.
It does not solve the problem — it searches a space too large to check exhaustively.
Where it earns its place
- The search space is far too large to try every option
- There is no formula that gives the answer directly
- But any candidate can be scored — you can tell a good one from a bad one
- A good enough answer is acceptable; the perfect one is not required
- Timetabling, route planning, aerial design, circuit layout
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Population
Fitness
Selection
Crossover
Mutation
Crossover and mutation do different jobs: Crossover recombines what the population already has — it explores between existing solutions.
Mutation introduces something genuinely new. Without it the population converges on a blend of its starting material and can never reach anything outside it.
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| Setting | Too low | Too high |
|---|---|---|
| Mutation rate | Converges early on a local optimum | Becomes a random search — good solutions are destroyed |
| Population size | Too little variety to recombine | Each generation is slow |
| Selection pressure | Good solutions spread too slowly | The population becomes clones and the search stalls |
The fitness function is the whole design: The algorithm optimises the fitness function exactly — the same failure mode as a reinforcement reward.
Score a timetable only on "no clashes" and it may return one that puts every lesson at 8am. It satisfied what you asked for.
No guarantee of the best answer: A genetic algorithm returns a good solution, not a provably optimal one, and two runs can give different answers.
Saying that is usually worth a mark — it is the honest limitation.
How this is tested — you must name the five stages in order and justify the approach from the size of the search space. It comes up two ways:
Paper 1 Section A
- Describe how a genetic algorithm works, 4-6 marks
- Name the stages of the cycle
- State the role of mutation
Paper 1 Section B — case study
- Justify a genetic algorithm for a scenario
- Explain a limitation of the result
The classic trap: Omitting mutation, or describing it as noise. Crossover only recombines what is already present — mutation is the only source of anything new, and the only escape from a local optimum.
One generation: score, select, cross over, mutate, repeat.
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A school must build a timetable from thousands of possible arrangements. Describe how a genetic algorithm would approach this, and state one limitation.
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