2. Steady state Selection
Big part of chromosome should survive to next generation
Example:
Crossover
Genetic operator that combine (mate) two chromosome (parent) to produce new
chromosome (offspring).
New chromosome will has better characteristic than the parents
Types
 One point crossover
 Two point crossover
 Uniform
 Arithmetic
 Heuristic
Single point crossover - one crossover point is selected, binary string from beginning of
chromosome to the crossover point is copied from one parent, the rest is copied from the
second parent

3. =
Two point crossover - two crossover point are selected, binary string from
beginning of chromosome to the first crossover point is copied from one parent, the
part from the first to the second crossover point is copied from the second parent
and the rest is copied from the first parent =
Uniform crossover - bits are randomly copied from the first or from the second
parent =
Arithmetic crossover - some arithmetic operation is performed to make a new
offspring
= (AND)
Heuristic Crossover
Use formula to generate the offspring
Offspring1 =BestParents + r * (BestParents – WorstParents)
OffSpring2= BestParents
R random number between 0 and 1

4. chromosome to prevent population to go to local optima Mutation types
 Genetic operator that is used to create new generation by changing some bits in
chromosome to prevent population to go to local optima
Mutation types
 Flip mutation
 Swap mutation
 Scramble mutation
 Inversion mutation
 Insert mutation

6. Crossover and Mutation Probability (Practically)
 Crossover Probability
o 0%  same parents
o 100%--> whole chromosome changed
o Best as in practice 80%-95%
 Mutation probability
o Best is .5% - 1%
Other parameters affect to GA
 Population size
o Very big not good
o and sometimes
 Selection
o Basic roulette wheel and some times rank is best
 Encoding
o Depend on problem