Application of A.I. lecture_2

The Steady State Genetic Algorithm (ssGA) differs from the generational model in that there is typically one single new member inserted into the new population at any time.
A Replacement strategy defines which member of the population will be replaced by the new offspring ( worst, Oldest or Random individual).
The better performance of the ssGA can be explained by the fact that an offspring is immediately used as a part of the mating pool making a shift towards the optimal solution possible in a relatively early phase of the optimization process.
Initialization [Population]
Evaluation [ Population]
Gen=0
Do
Selected_Parents = Selection[Population]
Created_Offspring= Recombination[selected_parents]
Mutation[Created_Offspring]
Population = Created_Offspring+Replacement[Population]
Evaluation[Created_Offspring]
Gen=Gen+1
Until Stop_Criterion
The main difference between the sGA and ssGA is:
Selection : in this algorithm we can mainly used tournament selection .
Crossover : we can mainly used (2X) or (UX) .
Mutation : we can use high probability for mutation because ssGA can work with high mutation value duo to the Replacement strategies.
Replacement : the main idea in ssGA is the replacement strategy