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Course Outline
Day 1:
- What is a genetic algorithm?
- Chromosome fitness
- Choosing the random initial population
- The crossover operations
- A numeric optimzation example
Day 2
- When to use genetic algorithm
- Coding the gene
- Local maximums and mutation operation
- Population diversity
Day 3
- The meaning and effect of each genetic algorithm parameter
- Varying genetic parameters
- Optimizing scheduling problems
- Cross over and mutation for scheduling problems
Day 4
- Optimizing program or set of rules
- Cross over and mutation operations for optimizing programs
- Creating a parallel model of the genetic algorithm
- Evaluating the genetic algorithm
- Applications of genetic algorithm
Requirements
Basic understanding of search problems and optimization
Testimonials
There was a lot of information and a lot of real world examples that helped to better explain the content from the optaplanner userguide. If we ever had issues understanding something or wanted further information into certain topics he was happy to look into it and get back to us after a break or the following lecture. The option to focus in on areas we were more interested in was super useful.
Exel Computer Systems plc
The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.
Terry Strachan - Exel Computer Systems plc
The discussions to broaden our horizons
FAB banak Egypt
The course is very interesting being the main focus nowdays
mohamed taher - FAB banak Egypt
Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course
Mohamed Ghowaiba - FAB banak Egypt
Helpful and good listener .. interactive
Ahmed El Kholy - FAB banak Egypt
Subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
Learning new language.
FAB banak Egypt
Los ejemplos que nos ponía.
JONATHAN MARIANO, si
los experimentos
JONATHAN MARIANO, si
Los ejercicios y ejemplos expuestos.
Marcos - JONATHAN MARIANO, si
Los temas de Machine Lerning.
Víctor Edgar - JONATHAN MARIANO, si
La actitud del.profe
Ivonne Guadalupe Avendaño Hernandez - JONATHAN MARIANO, si
Los conceptos enseñados fueron claros, prácticos y ayudan mucho en hacerse una idea de como utilizar este tema de IA & ML
Miguel - JONATHAN MARIANO, si
La experiencia y conocimientos del instructor.
SERGIO BRAVO - JONATHAN MARIANO, si
Quizá algo de practica.
Hilario García - JONATHAN MARIANO, si
uno de las practicas
JONATHAN MARIANO, si
El conocimiento y manejo del tema del instructor
Zaira N. - JONATHAN MARIANO, si
lo innovador porque es algo que ya lo estamos viviendo.
jesus isaias - JONATHAN MARIANO, si
A lot of exercises and chances to test different features. Good introduction to Azure AI applications.
EY GDS
Exercises & hands-on labs!
EY GDS
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