This repository will cover the material of the Computational Intelligence class. In this class, we will show how you could implement Metaheuristic Algorithms in python. So If you are interested in Metaheuristic Algorithms, This is the place for you. (The goal of making this content was anyone can be able to learn Metaheuristic Algorithms on their own, So if you can't be present in the class, you can learn all of the material here.)
Before you start this tutorial, you should know:
- Python - If you don't know python, but you know how to program, you can learn it in first week of this class.
- Numpy Library - If you don't know Numpy, you can learn it in first week of this class.
- Algorithms & Datastructures - If you don't know Algorithms & Datastructures, you can learn it here.
- Python
- Basic Data Types
- Containers
- Functions
- Classes
- Numpy
- Creating Arrays
- Array Data
- Reshaping an Array
- Arithmetic Operations on Arrays
- Conditional Operators
- Mathematical and Statistical Functions
- Array Indexing
- Iterating
- Saving and loading
First three steps of Implementing Genetic Algorithms :
- Population Initialization
- Fitness Calculation
- Selection Operators
- Introduction to Knapsack problem
- How you can create a fake data for Knapsack problem
- Last two steps of implementing the Genetic Algorithms :
- Crossover
- Mutation
- Solving Knapsack problem with Brute Force Algorithm
- Solving Knapsack problem with Genetic Algorithm
- Visualization of Genetic Algorithm output
- How to find the minimum point of various functions
- How to represent a floating-point chromosome as a binary chromosome
- Uniform Mutation (Floating-point representation)
- Linear Recombination Crossover
- Genetic Algorithm (Floating-point Representation)
- 3-D Visualization of every function & its fitness