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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
28 Hours