# Q1(c) Mutation operators [5]

Subject: Intelligent Systems II

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/is2/#tee/papers/paper-2024-final/q1c

Canonical: https://deep-learning-study-room.vercel.app/read/is2/tee/papers/paper-2024-final--q1c/

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(c)** [5 marks · 2024-04-17]

Cite and illustrate various mutation operators in the genetic algorithm.

### Answer

**Mutation** randomly changes one or more genes to introduce diversity and explore solutions not obtained by crossover alone. Apply a small specified mutation probability; excessive mutation can destroy good structures.

| Operator | Before → after | Suitable representation |
| --- | --- | --- |
| Bit flip | 10110 → 10010 (third bit 1→0) | Binary chromosome |
| Random resetting | [2,7,4] → [2,3,4] (replace gene 2 by a valid value) | Integer/discrete |
| Swap | A B C D E → A D C B E (swap positions 2 and 4) | Permutation / TSP |
| Inversion | A | B C D | E → A | D C B | E | Permutation / TSP |
| Insertion | A B C D E → A D B C E (remove D and insert after A) | Permutation / TSP |
| Gaussian perturbation | [1.2,2.0] → [1.2,2.0+0.1]=[1.2,2.1] | Real-valued; respect bounds |

For a TSP route, arbitrary bit or value replacement can duplicate a city and omit another. Swap/inversion preserve the permutation, so the resulting route remains valid. Mutation changes a chromosome; crossover combines material from two parents.