---
title: "Crossover and mutation in GA"
subject: "Data Mining"
chapter: 3
key: "semester_7/data_mining:crossover-mutation-ga"
source: https://www.bctengineer.com/q/semester_7/data_mining/crossover-mutation-ga
site: BCT Engineering
---

# Crossover and mutation in GA

Data Mining · Chapter 3: Classification

- Asked in: year not given, 4 marks
- Video: [Genetic Algorithm with Solved Example(Selection,Crossover,Mutation)](https://www.youtube.com/watch?v=InVJWW_NzFY) — btech tutorial
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/crossover-mutation-ga
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-3

## Question

Write a short note on crossover and mutation in Genetic Algorithm.

## Short answer

Crossover combines two parent chromosomes with high probability (e.g., 0.6–0.9) to produce offspring, exploiting good building blocks. Mutation randomly flips bits with very low probability (e.g., 0.001–0.01) to maintain diversity and escape local optima.

## Answer

In a **Genetic Algorithm** a new generation is produced from the current population using the genetic operators crossover and mutation, after the fitter individuals have been selected as parents. Each solution is encoded as a chromosome, usually a bit string.

#### Crossover (recombination)
Crossover combines the genetic material of two parent chromosomes to produce one or two children that inherit features of both. It is applied with a high probability (typically 0.6 to 0.9) and exploits the good parts of existing solutions.

- **Single-point crossover**: choose a cut point and swap the tails.
- **Two-point crossover**: swap the segment between two cut points.
- **Uniform crossover**: each bit is taken from either parent at random.

```text
Parent 1: 1 1 0 0 | 1 0 1      Child 1: 1 1 0 0 | 0 1 0
Parent 2: 0 1 1 1 | 0 1 0      Child 2: 0 1 1 1 | 1 0 1
               cut point
```

#### Mutation
Mutation randomly changes one or a few bits of a chromosome (0 becomes 1 and the reverse), with a very small probability (e.g. 0.001 to 0.01 per bit). It restores lost genetic material, maintains **diversity** in the population and helps the search escape local optima.

```text
Before: 1 1 0 0 1 0 1
After : 1 1 0 1 1 0 1     (4th bit flipped)
```

| Basis | Crossover | Mutation |
| --- | --- | --- |
| Parents | Two | One |
| Probability | High | Very low |
| Role | Mixes good building blocks (exploitation) | Adds new variation (exploration) |

Too high a mutation rate makes the search random; too low a rate gives premature convergence.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. What does single-point crossover do?
   A. Flips one bit of one parent
   B. Swaps the whole chromosomes
   C. Chooses a cut point and swaps the tails of two parents
   D. Removes the weakest parent
   Answer: C. Children inherit parts of both parents. (Key point: Crossover)

2. What is the main purpose of mutation?
   A. Combine good building blocks of two parents
   B. Increase the crossover rate
   C. Compute fitness
   D. Maintain diversity and escape local optima
   Answer: D. It restores lost genetic material. (Key point: Mutation purpose)

3. Which probabilities are typical?
   A. Crossover high (0.6-0.9), mutation very low (0.001-0.01)
   B. Both very low
   C. Crossover very low, mutation high
   D. Both about 0.5
   Answer: A. Mutation is a rare event per bit. (Key point: Probabilities)

4. What happens if the mutation rate is too high?
   A. Premature convergence
   B. The search becomes random
   C. Crossover stops working
   D. Fitness always rises
   Answer: B. Too low gives premature convergence. (Key point: Mutation rate)

## Related in Data Mining

- [Short note: Genetic algorithm](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Agenetic-algorithm-note) — Data Mining, chapter 3

## Related in other subjects

- [Genetic algorithm and operators](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Agenetic-algorithm-operators) — Artificial Intelligence, chapter 6, asked 4×
- [Machine learning, GA, operators, areas](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aml-genetic-algorithm-operators-areas) — Artificial Intelligence, chapter 6, asked 2×
- [GA vs fuzzy based learning](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Aga-vs-fuzzy-learning) — Artificial Intelligence, chapter 6