Intelligent Systems II · TEE · papers
Q1(c) Mutation operators [5]
Final examination · 17 Apr 2024
Actual historical paper ·100 marks attempted /140 offered · Q1 compulsory + any four of Q2–Q7. Source defects and solution assumptions are explicitly identified.
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.