Mutation in Genetic Algorithm (Machine Learning)

In Genetic Algorithm (a part of Machine Learning), there's a step called mutation. Think of it like this — just like in real life genes can change, here too, we make small changes in the "chromosomes" (our data or solutions) to improve them.

In simple words, mutation means changing some part of a solution randomly to explore better options and avoid getting stuck at the same place.

There are many types of mutation. Let’s go through them one by one, with easy examples:

1. Bit-wise Mutation

In this, we flip some random bits (0 becomes 1, or 1 becomes 0). It works mainly when data is in 0s and 1s.

bit wise mutation

2. Random Setting Mutation

This is like bit mutation, but here we work with actual numbers (not just 0 or 1). We randomly change a value to something else.

Random Setting Mutation

3. Inversion Mutation

We pick a part of the chromosome and reverse the order of that part.

Inversion Mutation

4. Scramble Mutation

Here, we pick a part of the chromosome and shuffle it randomly, like mixing cards.

Scramble Mutation

5. Swap Mutation

We just pick two values and swap their places.

Swap Mutation