Optimization with Metaheuristics in Python
Optimization with Metaheuristics in Python
.MP4, AVC, 600 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 4h 27m | 2.2 GB
Instructor: Dana Knight
.MP4, AVC, 600 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 4h 27m | 2.2 GB
Instructor: Dana Knight
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, Tabu Search, and Evolutionary Strategies. 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!
The ideal student should have basic knowledge in Operation Research and basic programming skills.
Optimization with Metaheuristics in Python
