Description
Optimization with Metaheuristics in Python is the name of a training course from the Udemy website that fully teaches how to optimize using meta-heuristic algorithms, simulated refrigeration algorithms, genetic algorithms, forbidden search, and evolutionary strategies.
During this course, you will learn how to optimize in Python and learn to work with meta-heuristic algorithms. Sometimes, in complex problems, using a deterministic algorithm does not lead you to the best and most optimal solution. In such a situation, using other algorithms is the most logical solution to reach the answer and solve the problem.
This training course provides you with a lot of information in the field of meta-heuristic algorithms and uses four main techniques such as simulated cooling algorithm, genetic algorithm, forbidden search, and evolutionary strategy to optimize the solution in Python language. By watching this course, you can learn how to code these algorithms in Python and get to know how to manage constraints using the penalty method.
What you will learn in the course Optimization with Metaheuristics in Python:
- Teaching the main topics of optimization
- Familiarity with meta-heuristic algorithms
- Simulated refrigeration algorithm, genetic algorithm, forbidden search, and evolutionary strategy
- Coding meta-heuristic algorithms in Python language
- Management of constraints using the penalty method
Course details
Publisher: Udemy
Instructor: Curiosity for Data Science
Language: English
Level of training: introductory to advanced
Number of courses: 83
Training duration: 9 hours and 52 minutes
Optimization with Metaheuristics in Python Course headings
Prerequisite for the course Optimization with Metaheuristics in Python
Basic knowledge in Operations Research and Optimization – (not a must, but helpful)
Basic programming skills in Python – (not a must, but helpful)
Pictures
Installation guide
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English subtitle
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Download part 1 – 2 GB
Download part 2 – 2 GB
Download part 3 – 686 MB
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