Genetic Algorithms Training Course
This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
- What is a genetic algorithm?
- Chromosome fitness
- Choosing the random initial population
- The crossover operations
- A numeric optimzation example
- When to use genetic algorithm
- Coding the gene
- Local maximums and mutation operation
- Population diversity
- The meaning and effect of each genetic algorithm parameter
- Varying genetic parameters
- Optimizing scheduling problems
- Cross over and mutation for scheduling problems
- 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
The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.
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