Optimization with Metaheuristics in Python (Update)
Bestseller | h264, yuv420p, 1280x720|ENGLISH, aac, 44100 Hz, stereo | 9h 52 mn | 5.56 GB
Created by: Curiosity for Data Science
Learn Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies, and Learn to Handle Constraints 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
Requirements
Basic knowledge in Operations Research and Optimization - (not a must, but helpful)
Basic programming skills in Python - (not a must, but helpful)
Description
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
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