Formation Genetic Algorithms

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