Mealpy Tutorial (Full version)

How to use open-source software "mealpy". (Full version)
Or
+ Part 1: https://www.youtube.com/watch?v=wh-C-...
+ Part 2: https://youtu.be/TAUlSykOjeI
Link code: https://github.com/thieu1995/mealpy

▬▬▬▬▬▬▬▬▬ Contents of this tutorial ▬▬▬▬▬▬▬▬▬▬

0:00 - Intro
0:19 - Download and install Miniconda on Windows 11
1:22 - Create a new environment using Miniconda
2:32 - Install Mealpy
5:08 - Pycharm and set environment on it
9:22 - Introducing the structure of Mealpy library
10:16 - The Optimizer class
10:50 - The Problem class
11:44 - The Termination class
15:10 - The History class (How to draw figures)
16:37 - How to import the mealpy library (Optimizer class)
18:32 - Define a problem dictionary (problem instance of Problem class)
19:32 - Define objective-function
21:18 - Problem definition (Find minimum of Fx function)
23:10 - How to call an optimizer to solve optimization problem
25:38 - The Problem class
26:23 - Sequential, Thread and Process training mode setting
28:23 - Explaining the current best and global best (training output)
29:18 - How to get final fitness and final position (solution)
30:38 - The structure of the "solution" attribute in Optimizer class
33:48 - Other ways to pass Lowerbound and Upperbound in problem dictionary
36:05 - How to import and define the Termination object
43:08 - Time-bound termination object
45:16 - Early Stopping termination object
47:18 - How to use Sequential/MultiThreading/MultiProcessing training mode
51:58 - Fix error with MultiProcessing training mode
55:54 - How to deal with Multi-objective Optimization Problem
1:05:09 - How to deal with Constrained Optimization Problem
1:11:46 - How to draw some important figures using History object
1:23:15 - How to use Mealpy to optimize hyper-parameters of a model
1:26:15 - Using Mealpy to optimization hyper-parameters of a traditional SVM classification
1:30:18 - Brute force method for tunning hyper-parameters
1:36:18 - GridSearchCV method for tunning hyper-parameters
1:39:28 - Metaheuristic Algorithm method for tunning hyper-parameters