Optimization with Metaheuristics in Python

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Optimization with Metaheuristics in Python

Learn Simulated Annealing, Genetic Algorithm, and Tabu Search

This course will guide you on what optimization is and what metaheuristics are. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you’d like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used.

This course covers information on metaheuristics and three widely used techniques which are Simulated Annealing, Genetic Algorithm, and Tabu Search. By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, and Tabu Search are, why they are used, how they work, and best of all, how to code them in Python!

What Will I Learn?

  • Learn the foundations of optimization
  • Understand metaheuristics such as Simulated Annealing, Genetic Algorithm, and Tabu Search
  • Be able to code metaheuristics in Python

Who is the target audience?

  • Anyone who wants to learn about metaheuristics
  • Anyone who wants to learn Genetic Algorithm
  • Anyone who wants to learn Simulated Annealing
  • Anyone who wants to learn Tabu Search
  • Anyone who wants to code metaheuristics in Python

About this Optimization with Metaheuristics in Python Course

  • Instructor: Dana Knight
  • Course Length: 4 hours
  • Course Language: English
  • Price: $150

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