Optimization with Metaheuristics in Python

Learn Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies, and Learn to Handle Constraints

4.24 (987 reviews)

Udemy

platform

English

language

Data & Analytics

What you will 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

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Comidoc Review

Our Verdict

This course offers a comprehensive introduction and hands-on experience with various optimization metaheuristics, providing you the tools and theoretical knowledge to develop your own solutions. It is slightly weighed down by overly complex code implementations, which could be more streamlined for clarity. With attention given to refining these areas, this already valuable course will become even stronger in helping learners solidify their understanding of optimization techniques with Python.

What We Liked

Covers a wide range of optimization algorithms including Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies
Detailed explanations and step-by-step demonstration of each algorithm for both continuous and combinatorial problems
Includes code implementation from scratch in Python with clear explanations
Provides real-world problem applications and instills confidence to develop more advanced algorithms

Potential Drawbacks

Code can be overly complex and convoluted, making it hard to follow
Some concepts are not explained precisely enough or could benefit from being more brief
Limited coverage of different constraint handling methods
Instructor's speech may sometimes lack confidence in delivery