Optimization with Metaheuristics in Python
4.3 (261 ratings)
1,536 students enrolled
Created by Curiosity for Data Science
What you'll learn
- Learn the foundations of optimization
- Understand metaheuristics such as Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies
- Be able to code metaheuristics in Python
- Handle constraints though penalties
- Basic knowledge in Operations Research and Optimization - (not a must, but helpful)
- Basic programming skills in Python - (not a must, but helpful)
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 four widely used techniques which are:
- Simulated Annealing
- Genetic Algorithm
- Tabu Search
- Evolutionary Strategies
By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! With no packages and no libraries, learn to code them from scratch!! You will also learn how to handle constraints using the penalty method.
Here's the awesome part --> you do NOT need to know Python programming!
- This course will teach you how to optimize continuous and combinatorial problems using Python
- Where every single line of code is explained thoroughly
- The code is written in a simple manner that you will understand how things work and how to code the algorithms even with zero knowledge in Python
Basically, you can think of this as not only a course that teaches you 4 well known metaheuristics, but also Python programming!
Please feel free to ask me any question! Don't like the course? Ask for a 30-day refund!!