The description
Optimization with Metaheuristics in Python is the name of a Udemy tutorial that teaches how to optimize with extra-heuristic algorithms, simulated chilling algorithm, genetic algorithm, forbidden search, and evolutionary strategy. During this course, you will learn how to optimize the Python language and learn how to work with heuristic algorithms. Sometimes in complex problems, using a deterministic algorithm will not give you the best and most optimal solution. In such a situation, using other algorithms is the most logical solution to the problem.
This course provides you with a lot of information about meta-algorithm algorithms and uses four basic techniques such as simulated refrigeration algorithm, genetic algorithm, forbidden search and evolutionary strategy to optimize the solution in Python language. By watching this course, you will be able to learn how to code these algorithms in the Python language and learn how to handle constraints using the penalty method.
What you will learn in Optimization with metaheuristics in Python:
- Discover the main themes of optimization
- Introduction to extra-heuristic algorithms
- Simulated refrigeration algorithm, genetic algorithm, forbidden search and evolution strategy
- Coding of extra-heuristic algorithms in Python language
- Managing Restrictions Using the Penalty Method
Specifications of the course Optimization with metaheuristics in Python:
Editor: Udemy
Instructors: Curiosity for Data Science
French language
Level: Introductory to Advanced
Number of lessons: 83
Duration: 9 hours and 52 minutes
Course titles
Course prerequisites
Basic knowledge of operations research and optimization – (not essential, but useful)
Basic Python programming skills – (not essential, but helpful)
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english subtitle
Quality: 720p
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File password(s): ngaur.com
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4.6 GB