
Explore the basic idea of genetic algorithm based optimization. Represent solutions as genes and chromosomes, breeding a population across generations to approach optimal results, and note pros, cons, and applications.
Evaluate the new generation in a genetic algorithm by averaging, crossover, and mutation of design parameters, using the objective function to assess performance and preserve elites with an elitist approach.
Examine how average probability, crossover probability, mutation magnitude and dampening, and population size influence convergence and the global maximum in a ten-generation seeded genetic algorithm.
Time-to-application is number one goal of this course --> After the course you can directly start optimizing using a ready-to-use Python based Genetic Algorithm Tool!
No time consuming "tool development from scratch" --> we work with ready-to-use, flexible Genetic Algorithm Optimization Tool written in the most basic Python and you get the tool at the end of the course
For all the "Beginners" who want to be real-world users in the most effective way possible
No advanced Python or programming skills needed --> Most basic Python is used for the whole algorithm --> Python lists and Numpy arrays thats it!
Designed for Beginners who don't want to program the algorithm or spent a lot of time to transfer someones hard-coded program to their individual optimisation problems
Complete Beginners Guide to Genetic Algorithm Optimization
Do you want to learn about a very powerful optimization method that is used for many optimization problems such as neural networks, engineering, logistics, finance and many more? Do you want to avoid a ton of theory without practical application or very specific code snippets that are not really transferable to your individual optimization problems?
If so, this course will help you enhance your optimization skills with:
- Genetic Algorithm based Optimization
- A ready-to-use python based simple but flexible Genetic Algorithm Based Optimizer
Genetic algorithm based optimization is a metaheuristic optimization method for a large specturm of optimization problems with multiple design parameters (multi-parameter). Compared to other optimization methods it is more stable against local extrema and offers great flexibility.
This course is designed so that you can apply GA optimization as fast as possible to your problems. A tool is provided that out-of-the-box is already able to solve many types optimization problems without the need to start from scratch or modify a lot.
This course offers:
A complete guide to Genetic Algorithm based Optimization for beginners
Almost no preliminary knowledge in programming and optimization required
Optimizer example build with most basic Python programming
Easy-to-understand step-by-step guide to Genetic Algorithm based Optimization
Practical knowledge, no dry theory
Hands-on step-by-step optimization validation case
Hands-on solving of real-world optimization problem
Ready-to-use, free GA based Optimization Tool based on python, which is not hard-coded and therefor flexible and usable for your multi-parameter optimization problems