Welcome to the Course on Genetic Algorithms! This course offers a comprehensive introduction to genetic algorithms and guides you through implementing them in Python. Genetic algorithms are powerful optimization tools inspired by the process of natural selection, widely used in fields ranging from machine learning to complex optimization problems.
This course is structured to provide a step-by-step introduction to the core concepts of genetic algorithms. You will learn how to implement genetic algorithms in Python and apply them to solve optimization problems such as creating a agent to play Flappy Bird. The course covers the following topics:
- Introduction to Genetic Algorithms: Fundamental concepts and applications of genetic algorithms.
- Genetic Algorithm Implementation: How to implement genetic algorithms in Python.
- Selection: Techniques for selecting individuals for the next generation based on fitness.
- Crossover: Methods for combining the genomes of two parents to produce offspring.
- Mutation: Strategies for introducing random variations to maintain genetic diversity.
- Python Programming: Basic understanding of Python programming.
Feel free to modify the code and experiment. Playing with the parameters and code will help deepen your understanding.
This course was created by one of our most skilled members Tobias Fremming to share his knowledge and expertise with the rest of the members of Cogito NTNU. Tobias is a talented programmer and a passionate teacher. He has a deep understanding of genetic algorithms and has a knack for explaining complex concepts in a simple and easy-to-understand manner.
 Tobias Fremming |