Genetic Algorithm: A Simple Example

Introduction

Genetic Algorithm (GA) can sometimes be a bit difficult to understand !! :(

In this article, I’ll help you understand GA with a simple example. So don’t worry. Hang tight. All will be clear soon !! :)

Below are the steps to be followed to solve any optimization problem with the help of GA.

• Step 1- Choose an encoding technique, a selection operator, and a crossover operator
• Step 2- Choose a population size
• Step 3- Randomly choose the initial population
• Step 4- Select parental chromosomes
• Step 5- Perform Crossover (random crossover points)
• Step 6- Evaluation of offsprings
• Step 7- Repeat the process

Now we’ll see an example of a simple optimization problem and try to solve it with the help of the steps mentioned above.

Question

Answer

Step 1-

• Encoding technique- Binary encoding
• Selection operator- Roulette Wheel Selection
• Crossover operator- Single point crossover

Step 2-

Population size (n) = 4

Step 3-

Initial population (x value) = 13, 24, 8, 19

Step 4-

We see that if the Roulette wheel is spun four times, we’ll get 24 twice and 13 and 19 once. So possible parental combinations are (24,13) and (24,19).

Step 5-

Step 6-

We can see that the maximum f(x) value has increased from 576 to 729.

Step 7-

Now we’ll take these four offsprings as parents and repeat the process until our termination condition is not satisfied.

That will be it for this article.

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If you want to learn more about GA, check out my series of articles:

Genetic Algorithm (GA) series

If you have any questions or would like something clarified, you can find me on LinkedIn.

~happy learning.