# Q1(b) Genetic algorithm applications [5]

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/papers/paper-2025-april/q1b

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/papers/paper-2025-april--q1b/

Actual historical paper ·100 marks attempted /140 offered · Q1 compulsory + any four of Q2–Q7. Source defects and solution assumptions are explicitly identified.

**Question. Q1(b)** [5 marks · 2025-04-28]

List the applications of genetic algorithm and explain any one application in detail.

### Answer

**Genetic algorithm (GA)** is a population-based search method that evolves encoded candidate solutions using fitness evaluation, selection, crossover and mutation.

- Job-shop scheduling and timetable construction.
- Vehicle routing and travelling-salesman-type optimization.
- Feature selection and hyperparameter search in machine learning.
- Engineering design, such as selecting dimensions subject to strength or cost constraints.
- Resource allocation and placement problems.

### Detailed application: examination timetable

| Stage | How it is used |
| --- | --- |
| Representation | A chromosome assigns a time slot and room to every examination. A gene identifies one exam's assignment. |
| Fitness | Penalize student clashes, room-capacity violations and unavailable slots; among feasible schedules, reward spread-out exams and efficient room use. |
| Initial population | Create several different candidate timetables, using random assignments or constructive rules. |
| Selection | Prefer feasible and lower-penalty timetables while retaining diversity. |
| Crossover | Combine assignments from two parents; repair conflicts if the operator can violate feasibility. |
| Mutation | Move an exam to another permitted slot or swap compatible assignments. Recheck constraints. |
| Replacement / stopping | Keep useful candidates, often protecting the best feasible schedule; stop at a generation/time limit or sufficient improvement. |

**Illustrative example:** two exams sharing students cannot occupy the same slot. A candidate with that clash receives a strong penalty or is repaired before acceptance. This shows why fitness must reflect the actual constraints, not merely how many rooms are occupied.

**Benefit:** explores many combinations without exhaustively listing all timetables. **Limitation:** stochastic search can stagnate and does not guarantee a global optimum; poorly designed penalties can reward unusable schedules.